---------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/claywebb/Dropbox/KU/Research/Sanctions/The Domestic Economic Costs of Sanctions - A Firm Level Analysis/Replication Materials f
> or The Domestic Economic Costs of Sanctions - A Firm Level Analysis/thedomesticcostsofsanctions.log
  log type:  text
 opened on:  11 Jun 2020, 23:33:57

.   
. * Create Directory for Latex Tables
.   mkdir rawtables

.   
.   * Note: The files in "/rawtables" are used to create the tables that appear in the
.   * appendix but the tables have to be edited. The portmanteau tests were added
.   * personally and the tables were edited fore aesthetics.
.   
. * Load Data
.   use "rcs.dta"

.   
. * Set Data
.   tsset date  
        time variable:  date, 02jan1968 to 09jan2020, but with gaps
                delta:  1 day

.   
. * Create Counter  
. *  gen t = _n (Note: This is the code used to create the variable t in the data set. 
.   
. * Appendix Tables (Section 4)
. 
.   * Eli Lilly (1989)
.   
.     * Generate Returns
.           gen lly_returns = ln(lly_close/lly_close[_n-1])
(3,150 missing values generated)

.     
.         * Sanctions
.           gen tsanction3 = 1 if date > td("04jun1989") & date < td("25may1990")
(12,847 missing values generated)

.           recode tsanction3(.=0)
(tsanction3: 12847 changes made)

.         
.     * Bond Market Crash
.           gen f13minicrash = 1 if date > td("11oct1989") & date < td("17oct1989")
(13,091 missing values generated)

.           recode f13minicrash(.=0)
(f13minicrash: 13091 changes made)

.           
.     * Limit Time Period
.       keep if date > td("30dec1988") & date < td("01jun1990")
(12,737 observations deleted)

.                 
.     * Set for analysis
.           tsset t
        time variable:  t, 5278 to 5634
                delta:  1 unit

.           
.         * Table A.1 Models
.           eststo clear

.           
.           eststo: arch lly_returns, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1022.1311  
Iteration 1:   log likelihood =  1022.7973  
Iteration 2:   log likelihood =   1022.856  
Iteration 3:   log likelihood =  1022.8775  
Iteration 4:   log likelihood =  1022.8839  
(switching optimization to BFGS)
Iteration 5:   log likelihood =   1022.886  
Iteration 6:   log likelihood =   1022.887  
Iteration 7:   log likelihood =   1022.887  

Time-series regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       0.00
Log likelihood =  1022.887                        Prob > chi2     =     0.9975

------------------------------------------------------------------------------
             |                 OPG
 lly_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
lly_returns  |
       _cons |   .0016936   .0007442     2.28   0.023     .0002351    .0031521
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0001193   .0385141     0.00   0.998     -.075367    .0756056
-------------+----------------------------------------------------------------
     /SIGMA2 |     .00019   7.70e-06    24.69   0.000     .0001749    .0002051
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    39.1524
 Prob > chi2(40)           =     0.5083

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    19.9227
 Prob > chi2(40)           =     0.9967

.           
.           eststo: arch lly_returns, ar(4)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1026.0412  
Iteration 1:   log likelihood =    1026.44  
Iteration 2:   log likelihood =  1026.4755  
Iteration 3:   log likelihood =   1026.489  
Iteration 4:   log likelihood =  1026.4927  
(switching optimization to BFGS)
Iteration 5:   log likelihood =   1026.494  
Iteration 6:   log likelihood =  1026.4946  
Iteration 7:   log likelihood =  1026.4946  

Time-series regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       6.82
Log likelihood =  1026.495                        Prob > chi2     =     0.0090

------------------------------------------------------------------------------
             |                 OPG
 lly_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
lly_returns  |
       _cons |   .0016835   .0006397     2.63   0.009     .0004296    .0029374
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L4. |  -.1423334   .0545068    -2.61   0.009    -.2491647    -.035502
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0001862   7.41e-06    25.13   0.000     .0001717    .0002008
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    29.7228
 Prob > chi2(40)           =     0.8828

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    19.8664
 Prob > chi2(40)           =     0.9968

.           
.           eststo: arch lly_returns, ar(4) arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1025.1626  
Iteration 1:   log likelihood =  1026.7709  
Iteration 2:   log likelihood =  1027.2591  
Iteration 3:   log likelihood =   1027.802  
Iteration 4:   log likelihood =  1027.8575  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1028.1323  
Iteration 6:   log likelihood =  1028.2391  
Iteration 7:   log likelihood =  1028.2689  
Iteration 8:   log likelihood =  1028.2693  
Iteration 9:   log likelihood =  1028.2693  

ARCH family regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       6.52
Log likelihood =  1028.269                        Prob > chi2     =     0.0107

------------------------------------------------------------------------------
             |                 OPG
 lly_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
lly_returns  |
       _cons |   .0016784   .0006522     2.57   0.010     .0004001    .0029567
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L4. |  -.1356076   .0531233    -2.55   0.011    -.2397274   -.0314878
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0393911   .0468142     0.84   0.400    -.0523631    .1311452
             |
       _cons |   .0001777   7.70e-06    23.06   0.000     .0001626    .0001928
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    29.0705
 Prob > chi2(40)           =     0.8995

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    14.1178
 Prob > chi2(40)           =     1.0000

.           
.           eststo: arch lly_returns, ar(4) arch(1) garch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1026.2007  
Iteration 1:   log likelihood =  1028.0574  
Iteration 2:   log likelihood =  1028.2699  
Iteration 3:   log likelihood =  1028.3462  
Iteration 4:   log likelihood =  1028.3834  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1028.4146  
BFGS stepping has contracted, resetting BFGS Hessian (0)
Iteration 6:   log likelihood =   1028.461  
Iteration 7:   log likelihood =  1028.4628  (backed up)
Iteration 8:   log likelihood =  1028.4633  (backed up)
Iteration 9:   log likelihood =  1028.4638  (backed up)
Iteration 10:  log likelihood =  1028.4639  (backed up)
Iteration 11:  log likelihood =  1028.4657  
Iteration 12:  log likelihood =  1028.4667  
Iteration 13:  log likelihood =  1028.4667  

ARCH family regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       6.65
Log likelihood =  1028.467                        Prob > chi2     =     0.0099

------------------------------------------------------------------------------
             |                 OPG
 lly_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
lly_returns  |
       _cons |    .001677   .0006538     2.56   0.010     .0003954    .0029585
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L4. |  -.1368925   .0530712    -2.58   0.010    -.2409102   -.0328749
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0435809   .0524714     0.83   0.406    -.0592612     .146423
             |
       garch |
         L1. |  -.3711905   .6161787    -0.60   0.547    -1.578879    .8364976
             |
       _cons |   .0002453   .0001135     2.16   0.031     .0000229    .0004677
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    29.1066
 Prob > chi2(40)           =     0.8986

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    13.9854
 Prob > chi2(40)           =     1.0000

.           
.           eststo: arch lly_returns, ar(4) arch(22)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1027.5883  
Iteration 1:   log likelihood =  1028.0864  
Iteration 2:   log likelihood =  1028.1064  
Iteration 3:   log likelihood =   1028.111  
Iteration 4:   log likelihood =  1028.1124  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1028.1129  
Iteration 6:   log likelihood =  1028.1131  
Iteration 7:   log likelihood =  1028.1131  

ARCH family regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       8.11
Log likelihood =  1028.113                        Prob > chi2     =     0.0044

------------------------------------------------------------------------------
             |                 OPG
 lly_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
lly_returns  |
       _cons |   .0017478    .000647     2.70   0.007     .0004798    .0030159
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L4. |  -.1505991    .052894    -2.85   0.004    -.2542694   -.0469288
-------------+----------------------------------------------------------------
ARCH         |
        arch |
        L22. |   .0877856   .0481679     1.82   0.068    -.0066218     .182193
             |
       _cons |   .0001705   9.23e-06    18.47   0.000     .0001524    .0001886
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    30.9298
 Prob > chi2(40)           =     0.8477

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    21.3066
 Prob > chi2(40)           =     0.9933

.           
.           eststo: arch lly_returns, ar(4) arch(1) het(tsanction3 f13minicrash)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1047.8971  
Iteration 1:   log likelihood =  1049.0675  
Iteration 2:   log likelihood =  1049.3021  
Iteration 3:   log likelihood =  1049.3517  
Iteration 4:   log likelihood =  1049.3759  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1049.3784  
Iteration 6:   log likelihood =  1049.3808  
Iteration 7:   log likelihood =  1049.3808  
Iteration 8:   log likelihood =  1049.3808  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       9.05
Log likelihood =  1049.381                        Prob > chi2     =     0.0026

------------------------------------------------------------------------------
             |                 OPG
 lly_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
lly_returns  |
       _cons |   .0017795   .0006087     2.92   0.003     .0005865    .0029726
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L4. |  -.1413782   .0469847    -3.01   0.003    -.2334665     -.04929
-------------+----------------------------------------------------------------
HET          |
  tsanction3 |  -.1461676   .1405828    -1.04   0.298    -.4217048    .1293696
f13minicrash |   3.074528   1.086478     2.83   0.005     .9450711    5.203985
       _cons |  -8.651603   .1237353   -69.92   0.000     -8.89412   -8.409087
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0099189   .0631032     0.16   0.875    -.1137611     .133599
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    31.8860
 Prob > chi2(40)           =     0.8162

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    54.6181
 Prob > chi2(40)           =     0.0616

.           
.           eststo: arch lly_returns, ar(4) arch(1,22) het(tsanction3 f13minicrash)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1048.2225  
Iteration 1:   log likelihood =  1052.5153  
Iteration 2:   log likelihood =  1052.8315  
Iteration 3:   log likelihood =  1052.8641  
Iteration 4:   log likelihood =  1052.8657  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1052.8669  
Iteration 6:   log likelihood =   1052.879  
Iteration 7:   log likelihood =  1052.8791  
Iteration 8:   log likelihood =  1052.8791  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       9.95
Log likelihood =  1052.879                        Prob > chi2     =     0.0016

------------------------------------------------------------------------------
             |                 OPG
 lly_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
lly_returns  |
       _cons |   .0017206   .0005795     2.97   0.003     .0005848    .0028564
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L4. |  -.1401817   .0444473    -3.15   0.002    -.2272968   -.0530666
-------------+----------------------------------------------------------------
HET          |
  tsanction3 |  -.2769234   .1691206    -1.64   0.102    -.6083937    .0545469
f13minicrash |   3.262962   1.099717     2.97   0.003     1.107557    5.418367
       _cons |  -8.712144   .1261959   -69.04   0.000    -8.959484   -8.464805
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |    .029096   .0718302     0.41   0.685    -.1116886    .1698805
        L22. |   .1029759   .0413173     2.49   0.013     .0219955    .1839564
------------------------------------------------------------------------------
(est7 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    33.1094
 Prob > chi2(40)           =     0.7715

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    35.5593
 Prob > chi2(40)           =     0.6703

.           
.           eststo: arch lly_returns, ar(4) arch(16,22) het(tsanction3 f13minicrash)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1047.6676  
Iteration 1:   log likelihood =  1050.2305  
Iteration 2:   log likelihood =   1050.345  (backed up)
Iteration 3:   log likelihood =   1051.409  
Iteration 4:   log likelihood =  1051.6445  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1051.7572  
Iteration 6:   log likelihood =  1052.8367  
Iteration 7:   log likelihood =    1053.22  
Iteration 8:   log likelihood =  1053.3547  
Iteration 9:   log likelihood =  1053.4081  
Iteration 10:  log likelihood =    1053.41  
Iteration 11:  log likelihood =    1053.41  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =      10.66
Log likelihood =   1053.41                        Prob > chi2     =     0.0011

------------------------------------------------------------------------------
             |                 OPG
 lly_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
lly_returns  |
       _cons |   .0016637   .0005759     2.89   0.004     .0005351    .0027924
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L4. |  -.1474735   .0451693    -3.26   0.001    -.2360037   -.0589433
-------------+----------------------------------------------------------------
HET          |
  tsanction3 |  -.2477675   .1624393    -1.53   0.127    -.5661426    .0706077
f13minicrash |    3.19859   1.096276     2.92   0.004     1.049928    5.347252
       _cons |  -8.677442   .1203809   -72.08   0.000    -8.913384   -8.441499
-------------+----------------------------------------------------------------
ARCH         |
        arch |
        L16. |  -.0125156   .0214149    -0.58   0.559     -.054488    .0294567
        L22. |   .0971978   .0410268     2.37   0.018     .0167868    .1776089
------------------------------------------------------------------------------
(est8 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    33.2449
 Prob > chi2(40)           =     0.7663

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    35.9338
 Prob > chi2(40)           =     0.6538

.           
.           eststo: arch lly_returns, ar(4) arch(22) het(tsanction3 f13minicrash)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1048.2891  
Iteration 1:   log likelihood =  1052.2285  
Iteration 2:   log likelihood =  1052.6355  
Iteration 3:   log likelihood =  1052.6624  
Iteration 4:   log likelihood =  1052.6695  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1052.6699  
Iteration 6:   log likelihood =  1052.6715  
Iteration 7:   log likelihood =  1052.6715  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       9.82
Log likelihood =  1052.672                        Prob > chi2     =     0.0017

------------------------------------------------------------------------------
             |                 OPG
 lly_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
lly_returns  |
       _cons |   .0017111   .0005774     2.96   0.003     .0005794    .0028428
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L4. |   -.139918   .0446529    -3.13   0.002    -.2274362   -.0523999
-------------+----------------------------------------------------------------
HET          |
  tsanction3 |  -.2645815   .1617641    -1.64   0.102    -.5816333    .0524702
f13minicrash |   3.221577   1.095786     2.94   0.003     1.073876    5.369278
       _cons |  -8.683645   .1202534   -72.21   0.000    -8.919337   -8.447952
-------------+----------------------------------------------------------------
ARCH         |
        arch |
        L22. |   .1006144   .0415365     2.42   0.015     .0192044    .1820244
------------------------------------------------------------------------------
(est9 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    33.7598
 Prob > chi2(40)           =     0.7460

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    36.4631
 Prob > chi2(40)           =     0.6303

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant tsanction3 Sanction f13minicra
> sh MiniCrash) nomtitles title(Eli Lilly 1989) nodep

Eli Lilly 1989
-------------------------------------------------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)          (7)          (8)          (9)   
-------------------------------------------------------------------------------------------------------------------------------
lly_retu~s                                                                                                                     
Constant       0.002**      0.002***     0.002**      0.002**      0.002***     0.002***     0.002***     0.002***     0.002***
             (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)   
-------------------------------------------------------------------------------------------------------------------------------
ARMA                                                                                                                           
L.ar           0.000                                                                                                           
             (0.039)                                                                                                           
L4.ar                      -0.142***    -0.136**     -0.137***    -0.151***    -0.141***    -0.140***    -0.147***    -0.140***
                          (0.055)      (0.053)      (0.053)      (0.053)      (0.047)      (0.044)      (0.045)      (0.045)   
-------------------------------------------------------------------------------------------------------------------------------
SIGMA2                                                                                                                         
Constant       0.000***     0.000***                                                                                           
             (0.000)      (0.000)                                                                                              
-------------------------------------------------------------------------------------------------------------------------------
ARCH                                                                                                                           
L.arch                                   0.039        0.044                     0.010        0.029                             
                                       (0.047)      (0.052)                   (0.063)      (0.072)                             
L22.arch                                                           0.088*                    0.103**      0.097**      0.101** 
                                                                 (0.048)                   (0.041)      (0.041)      (0.042)   
L16.arch                                                                                                 -0.013                
                                                                                                        (0.021)                
L.garch                                              -0.371                                                                    
                                                    (0.616)                                                                    
Constant                                 0.000***     0.000**      0.000***                                                    
                                       (0.000)      (0.000)      (0.000)                                                       
-------------------------------------------------------------------------------------------------------------------------------
HET                                                                                                                            
Sanction                                                                       -0.146       -0.277       -0.248       -0.265   
                                                                              (0.141)      (0.169)      (0.162)      (0.162)   
MiniCrash                                                                       3.075***     3.263***     3.199***     3.222***
                                                                              (1.086)      (1.100)      (1.096)      (1.096)   
Constant                                                                       -8.652***    -8.712***    -8.677***    -8.684***
                                                                              (0.124)      (0.126)      (0.120)      (0.120)   
-------------------------------------------------------------------------------------------------------------------------------
N                357          357          357          357          357          357          357          357          357   
aic        -2039.774    -2046.989    -2048.539    -2046.933    -2048.226    -2086.762    -2091.758    -2092.820    -2093.343   
bic        -2028.141    -2035.356    -2033.028    -2027.545    -2032.715    -2063.495    -2064.614    -2065.676    -2070.077   
-------------------------------------------------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/lilly89.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant ts
> anction3 Sanction f13minicrash MiniCrash) nomtitles title(Eli Lilly 1989) nodep replace
(note: file rawtables/lilly89.tex not found)
(output written to rawtables/lilly89.tex)

.       
.   * Pfizer (1989)
.     
.         * Clear
.           clear

.           
.         * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen pfe_returns = ln(pfe_close/pfe_close[_n-1])
(3,148 missing values generated)

.     
.         * Sanctions
.           gen tsanction3 = 1 if date > td("04jun1989") & date < td("25may1990")
(12,847 missing values generated)

.           recode tsanction3(.=0)
(tsanction3: 12847 changes made)

.         
.     * Bond Market Crash
.           gen f13minicrash = 1 if date > td("11oct1989") & date < td("17oct1989")
(13,091 missing values generated)

.           recode f13minicrash(.=0)
(f13minicrash: 13091 changes made)

.           
.     * Limit Time Period
.       keep if date > td("30dec1988") & date < td("01jun1990")
(12,737 observations deleted)

.                 
.     * Set for analysis
.           tsset t
        time variable:  t, 5278 to 5634
                delta:  1 unit

.           
.         * Table A.2 Models
.           eststo clear

.           
.           eststo: arch pfe_returns, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1004.3064  
Iteration 1:   log likelihood =  1004.3623  
Iteration 2:   log likelihood =  1004.3708  
Iteration 3:   log likelihood =  1004.3722  
Iteration 4:   log likelihood =  1004.3725  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1004.3726  
Iteration 6:   log likelihood =  1004.3726  

Time-series regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       4.96
Log likelihood =  1004.373                        Prob > chi2     =     0.0259

------------------------------------------------------------------------------
             |                 OPG
 pfe_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
pfe_returns  |
       _cons |   .0003657   .0008718     0.42   0.675    -.0013431    .0020744
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0957955   .0430128     2.23   0.026      .011492    .1800991
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0002108   .0000103    20.38   0.000     .0001905    .0002311
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    39.9849
 Prob > chi2(40)           =     0.4709

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    39.6888
 Prob > chi2(40)           =     0.4841

.           
.           eststo: arch pfe_returns, ar(1) arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1008.0207  
Iteration 1:   log likelihood =  1008.6924  
Iteration 2:   log likelihood =  1008.7917  
Iteration 3:   log likelihood =  1008.8173  
Iteration 4:   log likelihood =  1008.8254  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1008.8279  
Iteration 6:   log likelihood =  1008.8292  
Iteration 7:   log likelihood =  1008.8294  
Iteration 8:   log likelihood =  1008.8294  

ARCH family regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       0.05
Log likelihood =  1008.829                        Prob > chi2     =     0.8267

------------------------------------------------------------------------------
             |                 OPG
 pfe_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
pfe_returns  |
       _cons |   .0009711   .0008368     1.16   0.246     -.000669    .0026111
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0141275   .0645251     0.22   0.827    -.1123394    .1405944
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1841266   .0581654     3.17   0.002     .0701246    .2981287
             |
       _cons |   .0001759   .0000126    14.00   0.000     .0001513    .0002006
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.4133
 Prob > chi2(40)           =     0.4520

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    31.3980
 Prob > chi2(40)           =     0.8327

.           
.           eststo: arch pfe_returns, ar(1) arch(1,3)

(setting optimization to BHHH)
Iteration 0:   log likelihood =   1011.322  
Iteration 1:   log likelihood =  1013.1265  
Iteration 2:   log likelihood =  1013.3812  
Iteration 3:   log likelihood =  1013.4094  
Iteration 4:   log likelihood =  1013.4147  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1013.4161  
Iteration 6:   log likelihood =  1013.4168  
Iteration 7:   log likelihood =  1013.4168  

ARCH family regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       0.28
Log likelihood =  1013.417                        Prob > chi2     =     0.5982

------------------------------------------------------------------------------
             |                 OPG
 pfe_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
pfe_returns  |
       _cons |   .0009538   .0008395     1.14   0.256    -.0006915    .0025992
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0366961   .0696282     0.53   0.598    -.0997727    .1731649
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1724609   .0663032     2.60   0.009     .0425091    .3024128
         L3. |   .0812665   .0377909     2.15   0.032     .0071978    .1553352
             |
       _cons |   .0001581   .0000129    12.24   0.000     .0001328    .0001834
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.1785
 Prob > chi2(40)           =     0.4623

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    21.4039
 Prob > chi2(40)           =     0.9930

.           
.           eststo: arch pfe_returns, ar(1) arch(1) garch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1007.4553  
Iteration 1:   log likelihood =  1011.0555  
Iteration 2:   log likelihood =   1011.379  
Iteration 3:   log likelihood =  1011.5001  
Iteration 4:   log likelihood =  1011.5368  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1011.5451  
Iteration 6:   log likelihood =  1011.5479  
Iteration 7:   log likelihood =   1011.548  
BFGS stepping has contracted, resetting BFGS Hessian (0)
Iteration 8:   log likelihood =  1011.5481  
Iteration 9:   log likelihood =  1011.5481  (backed up)
Iteration 10:  log likelihood =  1011.5481  (backed up)
Iteration 11:  log likelihood =  1011.5481  (backed up)
Iteration 12:  log likelihood =  1011.5481  
Iteration 13:  log likelihood =  1011.5481  

ARCH family regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       0.07
Log likelihood =  1011.548                        Prob > chi2     =     0.7857

------------------------------------------------------------------------------
             |                 OPG
 pfe_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
pfe_returns  |
       _cons |   .0008715   .0008254     1.06   0.291    -.0007464    .0024893
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0197374   .0725778     0.27   0.786    -.1225126    .1619873
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1658974   .0617467     2.69   0.007      .044876    .2869187
             |
       garch |
         L1. |   .3584387   .1793206     2.00   0.046     .0069767    .7099006
             |
       _cons |   .0001019   .0000301     3.39   0.001     .0000429    .0001608
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    39.6882
 Prob > chi2(40)           =     0.4842

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    23.5157
 Prob > chi2(40)           =     0.9824

.           
.           eststo: arch pfe_returns, ar(1) arch(1,3) het(tsanction3 f13minicrash)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1014.1964  
Iteration 1:   log likelihood =  1017.8352  
Iteration 2:   log likelihood =  1018.6349  
Iteration 3:   log likelihood =  1018.7516  
Iteration 4:   log likelihood =  1018.7788  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1018.7862  
Iteration 6:   log likelihood =  1018.7876  
Iteration 7:   log likelihood =  1018.7879  
Iteration 8:   log likelihood =  1018.7879  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       0.11
Log likelihood =  1018.788                        Prob > chi2     =     0.7446

------------------------------------------------------------------------------
             |                 OPG
 pfe_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
pfe_returns  |
       _cons |    .000988   .0007836     1.26   0.207    -.0005479     .002524
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0219684   .0674364     0.33   0.745    -.1102045    .1541413
-------------+----------------------------------------------------------------
HET          |
  tsanction3 |   .5708914   .1504905     3.79   0.000     .2759355    .8658472
f13minicrash |   .9289681   1.034033     0.90   0.369    -1.097699    2.955636
       _cons |  -9.129573   .1349934   -67.63   0.000    -9.394155   -8.864991
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1329522   .0662176     2.01   0.045     .0031681    .2627363
         L3. |   .0598993   .0327959     1.83   0.068    -.0043794     .124178
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.5775
 Prob > chi2(40)           =     0.4448

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    24.8250
 Prob > chi2(40)           =     0.9712

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant tsanction3 Sanction f13minicra
> sh MiniCrash) nomtitles title(Pfizer 1989) nodep

Pfizer 1989
---------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)   
---------------------------------------------------------------------------
pfe_retu~s                                                                 
Constant       0.000        0.001        0.001        0.001        0.001   
             (0.001)      (0.001)      (0.001)      (0.001)      (0.001)   
---------------------------------------------------------------------------
ARMA                                                                       
L.ar           0.096**      0.014        0.037        0.020        0.022   
             (0.043)      (0.065)      (0.070)      (0.073)      (0.067)   
---------------------------------------------------------------------------
SIGMA2                                                                     
Constant       0.000***                                                    
             (0.000)                                                       
---------------------------------------------------------------------------
ARCH                                                                       
L.arch                      0.184***     0.172***     0.166***     0.133** 
                          (0.058)      (0.066)      (0.062)      (0.066)   
L3.arch                                  0.081**                   0.060*  
                                       (0.038)                   (0.033)   
L.garch                                               0.358**              
                                                    (0.179)                
Constant                    0.000***     0.000***     0.000***             
                          (0.000)      (0.000)      (0.000)                
---------------------------------------------------------------------------
HET                                                                        
Sanction                                                           0.571***
                                                                 (0.150)   
MiniCrash                                                          0.929   
                                                                 (1.034)   
Constant                                                          -9.130***
                                                                 (0.135)   
---------------------------------------------------------------------------
N                357          357          357          357          357   
aic        -2002.745    -2009.659    -2016.834    -2013.096    -2023.576   
bic        -1991.112    -1994.148    -1997.445    -1993.707    -1996.432   
---------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/pfizer89.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant t
> sanction3 Sanction f13minicrash MiniCrash) nomtitles title(Pfizer 1989) nodep replace
(note: file rawtables/pfizer89.tex not found)
(output written to rawtables/pfizer89.tex)

.         
.   * Bed Bath and Beyond (2011)
.   
.     * Clear
.           clear

.           
.         * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen bbby_returns = ln(bbby_close/bbby_close[_n-1])
(7,475 missing values generated)

.     
.     * Currency Sanctions
.       gen csanction = 1 if date > td("11jul2011") & date < td("18oct2011")
(13,025 missing values generated)

.       recode csanction(.=0)
(csanction: 13025 changes made)

. 
.     * Debt Debate
.       gen debt = 1 if date > td("13may2011") & date < td("01aug2011")
(13,041 missing values generated)

.       recode debt(.=0)
(debt: 13041 changes made)

. 
.     * Black Mondayd             
.       gen bmonday = 1 if date > td("01aug2011") & date < td("15aug2011")
(13,085 missing values generated)

.       recode bmonday(.=0)
(bmonday: 13085 changes made)

. 
.     * Limit Time Period
.       keep if date > td("31dec2010") & date < td("03jan2012")   
(12,842 observations deleted)

.                 
.     * Set for analysis
.           tsset t
        time variable:  t, 10825 to 11076
                delta:  1 unit

.           
.         * Table A.3 Models
.           eststo clear

.           
.           eststo: arch bbby_returns

(setting optimization to BHHH)
Iteration 0:   log likelihood =  650.45019  
Iteration 1:   log likelihood =  650.45019  

Time-series regression

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  650.4502                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
bbby_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |    .000655   .0011699     0.56   0.576    -.0016381     .002948
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0003354   .0000173    19.34   0.000     .0003014    .0003694
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    24.6471
 Prob > chi2(40)           =     0.9730

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    13.7070
 Prob > chi2(40)           =     1.0000

.           
.           eststo: arch bbby_returns, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  651.54031  
Iteration 1:   log likelihood =  651.56358  
Iteration 2:   log likelihood =  651.56507  
Iteration 3:   log likelihood =  651.56519  
Iteration 4:   log likelihood =  651.56521  

Time-series regression -- AR disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       3.43
Log likelihood =  651.5652                        Prob > chi2     =     0.0641

------------------------------------------------------------------------------
             |                 OPG
bbby_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
bbby_returns |
       _cons |   .0006567   .0010665     0.62   0.538    -.0014336     .002747
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |    -.09399   .0507557    -1.85   0.064    -.1934693    .0054894
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0003324   .0000171    19.42   0.000     .0002989     .000366
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    25.0171
 Prob > chi2(40)           =     0.9692

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    11.6119
 Prob > chi2(40)           =     1.0000

.           
.           eststo: arch bbby_returns, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =   650.7331  
Iteration 1:   log likelihood =  651.48068  
Iteration 2:   log likelihood =   651.8024  
Iteration 3:   log likelihood =  651.84171  
Iteration 4:   log likelihood =  651.84662  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  651.84705  
Iteration 6:   log likelihood =  651.84721  
Iteration 7:   log likelihood =  651.84723  

Time-series regression -- ARMA disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =      14.48
Log likelihood =  651.8472                        Prob > chi2     =     0.0007

------------------------------------------------------------------------------
             |                 OPG
bbby_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
bbby_returns |
       _cons |   .0006546   .0011103     0.59   0.555    -.0015216    .0028308
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.6261182   .4133249    -1.51   0.130     -1.43622    .1839837
             |
          ma |
         L1. |   .5436313   .4482787     1.21   0.225    -.3349788    1.422241
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0003317   .0000183    18.13   0.000     .0002959    .0003676
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    22.9734
 Prob > chi2(40)           =     0.9858

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    10.7847
 Prob > chi2(40)           =     1.0000

.           
.           eststo: arch bbby_returns, ar(1) het(bmonday debt csanction)

(setting optimization to BHHH)
Iteration 0:   log likelihood =   661.7507  
Iteration 1:   log likelihood =   663.8811  
Iteration 2:   log likelihood =  664.42175  
Iteration 3:   log likelihood =  664.74551  
Iteration 4:   log likelihood =  665.10017  
(switching optimization to BFGS)
Iteration 5:   log likelihood =   665.2965  
Iteration 6:   log likelihood =  665.99396  
Iteration 7:   log likelihood =  666.00907  
Iteration 8:   log likelihood =  666.00986  
Iteration 9:   log likelihood =  666.00994  
Iteration 10:  log likelihood =  666.00994  

Time-series regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       0.52
Log likelihood =  666.0099                        Prob > chi2     =     0.4713

------------------------------------------------------------------------------
             |                 OPG
bbby_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
bbby_returns |
       _cons |   .0008619   .0010599     0.81   0.416    -.0012154    .0029393
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.0531748   .0738221    -0.72   0.471    -.1978633    .0915138
-------------+----------------------------------------------------------------
HET          |
     bmonday |   1.632755   .9425693     1.73   0.083    -.2146466    3.480157
        debt |  -.4174139   .1491781    -2.80   0.005    -.7097976   -.1250302
   csanction |   .1170088   .1945137     0.60   0.547     -.264231    .4982486
       _cons |   -8.12623   .0558853  -145.41   0.000    -8.235763   -8.016697
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    23.6652
 Prob > chi2(40)           =     0.9813

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    11.9966
 Prob > chi2(40)           =     1.0000

.           
.           eststo: arch bbby_returns, het(bmonday debt csanction)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  657.00645  
Iteration 1:   log likelihood =  662.16583  
Iteration 2:   log likelihood =  663.23385  
Iteration 3:   log likelihood =  664.81933  
Iteration 4:   log likelihood =  664.95515  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  665.10577  
Iteration 6:   log likelihood =  665.60414  
Iteration 7:   log likelihood =  665.65863  
Iteration 8:   log likelihood =  665.66171  
Iteration 9:   log likelihood =  665.66208  
Iteration 10:  log likelihood =  665.66209  

Time-series regression -- multiplicative heteroskedasticity

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  665.6621                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
bbby_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
bbby_returns |
       _cons |   .0008579   .0011134     0.77   0.441    -.0013243    .0030401
-------------+----------------------------------------------------------------
HET          |
     bmonday |   1.684005   .8944546     1.88   0.060    -.0690939    3.437104
        debt |  -.4180336   .1484319    -2.82   0.005    -.7089547   -.1271125
   csanction |   .0930033   .1934726     0.48   0.631    -.2861961    .4722027
       _cons |  -8.118579   .0558695  -145.31   0.000    -8.228082   -8.009077
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    22.9440
 Prob > chi2(40)           =     0.9860

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    12.2786
 Prob > chi2(40)           =     1.0000

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant csanction Sanction debt DebtDe
> bate bmonday BlackMonday) nomtitles title(Bed Bath and Beyond 2011) nodep

Bed Bath and Beyond 2011
---------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)   
---------------------------------------------------------------------------
bbby_ret~s                                                                 
Constant       0.001        0.001        0.001        0.001        0.001   
             (0.001)      (0.001)      (0.001)      (0.001)      (0.001)   
---------------------------------------------------------------------------
SIGMA2                                                                     
Constant       0.000***     0.000***     0.000***                          
             (0.000)      (0.000)      (0.000)                             
---------------------------------------------------------------------------
ARMA                                                                       
L.ar                       -0.094*      -0.626       -0.053                
                          (0.051)      (0.413)      (0.074)                
L.ma                                     0.544                             
                                       (0.448)                             
---------------------------------------------------------------------------
HET                                                                        
BlackMon~y                                            1.633*       1.684*  
                                                    (0.943)      (0.894)   
DebtDebate                                           -0.417***    -0.418***
                                                    (0.149)      (0.148)   
Sanction                                              0.117        0.093   
                                                    (0.195)      (0.193)   
Constant                                             -8.126***    -8.119***
                                                    (0.056)      (0.056)   
---------------------------------------------------------------------------
N                252          252          252          252          252   
aic        -1296.900    -1297.130    -1295.694    -1320.020    -1321.324   
bic        -1289.842    -1286.542    -1281.577    -1298.843    -1303.677   
---------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/bbby2011.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant c
> sanction Sanction debt DebtDebate bmonday BlackMonday) nomtitles title(Bed Bath and Beyond 2011) nodep replace
(note: file rawtables/bbby2011.tex not found)
(output written to rawtables/bbby2011.tex)

.   
.   * Gap (2011)
.   
.     * Clear
.           clear

.           
.         * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen gps_returns = ln(gps_close/gps_close[_n-1])
(5,759 missing values generated)

.     
.     * Currency Sanctions
.       gen csanction = 1 if date > td("11jul2011") & date < td("18oct2011")
(13,025 missing values generated)

.       recode csanction(.=0)
(csanction: 13025 changes made)

. 
.     * Debt Debate
.       gen debt = 1 if date > td("13may2011") & date < td("01aug2011")
(13,041 missing values generated)

.       recode debt(.=0)
(debt: 13041 changes made)

. 
.     * Black Mondayd             
.       gen bmonday = 1 if date > td("01aug2011") & date < td("15aug2011")
(13,085 missing values generated)

.       recode bmonday(.=0)
(bmonday: 13085 changes made)

.           
.         * Gap Report
.       gen report = 1 if date > td("18may2011") & date < td("24may2011")
(13,091 missing values generated)

.       recode report(.=0)  
(report: 13091 changes made)

. 
.     * Limit Time Period
.       keep if date > td("31dec2010") & date < td("03jan2012")   
(12,842 observations deleted)

.                 
.     * Set for analysis
.           tsset t
        time variable:  t, 10825 to 11076
                delta:  1 unit

.           
.         * Table A.4 Models
.           eststo clear

.           
.           eststo: arch gps_returns

(setting optimization to BHHH)
Iteration 0:   log likelihood =  582.63184  
Iteration 1:   log likelihood =  582.63184  

Time-series regression

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  582.6318                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 gps_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   -.000702   .0018344    -0.38   0.702    -.0042975    .0028934
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0005745   .0000198    29.01   0.000     .0005357    .0006134
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.5971
 Prob > chi2(40)           =     0.5790

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =     4.8936
 Prob > chi2(40)           =     1.0000

.           
.           eststo: arch gps_returns, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  582.42207  
Iteration 1:   log likelihood =  582.52769  
Iteration 2:   log likelihood =  582.60857  
Iteration 3:   log likelihood =  582.67592  
Iteration 4:   log likelihood =  582.72637  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  582.76911  
Iteration 6:   log likelihood =  582.93371  
Iteration 7:   log likelihood =  582.93451  
Iteration 8:   log likelihood =  582.93457  
Iteration 9:   log likelihood =  582.93457  

Time-series regression -- AR disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       0.36
Log likelihood =  582.9346                        Prob > chi2     =     0.5459

------------------------------------------------------------------------------
             |                 OPG
 gps_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
gps_returns  |
       _cons |  -.0007003   .0017494    -0.40   0.689     -.004129    .0027285
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.0490198   .0811781    -0.60   0.546    -.2081259    .1100864
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0005732   .0000211    27.10   0.000     .0005317    .0006146
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    35.3803
 Prob > chi2(40)           =     0.6781

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =     5.0386
 Prob > chi2(40)           =     1.0000

.           
.           eststo: arch gps_returns, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  582.63423  
Iteration 1:   log likelihood =  583.46794  
Iteration 2:   log likelihood =  584.49467  
Iteration 3:   log likelihood =  585.34517  
Iteration 4:   log likelihood =  585.68274  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  585.83264  
Iteration 6:   log likelihood =  585.92284  
Iteration 7:   log likelihood =  586.07143  
Iteration 8:   log likelihood =  586.10734  
Iteration 9:   log likelihood =  586.10935  
Iteration 10:  log likelihood =  586.10948  
Iteration 11:  log likelihood =  586.10948  

Time-series regression -- ARMA disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =     162.61
Log likelihood =  586.1095                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
 gps_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
gps_returns  |
       _cons |  -.0007025   .0017494    -0.40   0.688    -.0041313    .0027263
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |    -.90063   .1054668    -8.54   0.000    -1.107341   -.6939188
             |
          ma |
         L1. |   .8274992   .1369091     6.04   0.000     .5591624    1.095836
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0005589   .0000198    28.27   0.000     .0005202    .0005977
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    30.3992
 Prob > chi2(40)           =     0.8638

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =     4.3060
 Prob > chi2(40)           =     1.0000

.           
.           eststo: arch gps_returns, ar(1) ma(1) het(bmonday debt csanction report)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  617.24919  
Iteration 1:   log likelihood =   618.4551  
Iteration 2:   log likelihood =  618.89826  
Iteration 3:   log likelihood =  622.02514  
Iteration 4:   log likelihood =  623.11207  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  624.81845  
Iteration 6:   log likelihood =  629.49583  
Iteration 7:   log likelihood =  634.37596  
Iteration 8:   log likelihood =  636.23626  
Iteration 9:   log likelihood =  637.12532  
Iteration 10:  log likelihood =  639.27869  
Iteration 11:  log likelihood =  639.41983  
Iteration 12:  log likelihood =  639.96398  
Iteration 13:  log likelihood =   640.1327  
Iteration 14:  log likelihood =   640.1399  
(switching optimization to BHHH)
Iteration 15:  log likelihood =   640.1471  
Iteration 16:  log likelihood =  640.14776  
Iteration 17:  log likelihood =  640.14779  
Iteration 18:  log likelihood =   640.1478  
Iteration 19:  log likelihood =   640.1478  
(switching optimization to BFGS)
Iteration 20:  log likelihood =   640.1478  
Iteration 21:  log likelihood =  640.14781  

Time-series regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =     217.00
Log likelihood =  640.1478                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
 gps_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
gps_returns  |
       _cons |   .0004804   .0011574     0.42   0.678     -.001788    .0027488
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.9505823   .0888578   -10.70   0.000     -1.12474   -.7764242
             |
          ma |
         L1. |   .9329257   .1046204     8.92   0.000     .7278736    1.137978
-------------+----------------------------------------------------------------
HET          |
     bmonday |   1.881361   .4862565     3.87   0.000     .9283154    2.834406
        debt |  -.2099999   .1767347    -1.19   0.235    -.5563935    .1363937
   csanction |   .3946524   .2003296     1.97   0.049     .0020136    .7872912
      report |   3.920708   .8341997     4.70   0.000     2.285707    5.555709
       _cons |  -8.096163   .0864558   -93.65   0.000    -8.265613   -7.926713
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    31.3353
 Prob > chi2(40)           =     0.8347

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    28.7150
 Prob > chi2(40)           =     0.9080

.           
.           eststo: arch gps_returns, ar(1) het(bmonday debt csanction report)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  616.63694  
Iteration 1:   log likelihood =  620.08391  
Iteration 2:   log likelihood =  621.73007  
Iteration 3:   log likelihood =  622.86285  
Iteration 4:   log likelihood =  623.96338  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  624.99981  
Iteration 6:   log likelihood =  632.81829  
Iteration 7:   log likelihood =  637.65358  
Iteration 8:   log likelihood =  639.13551  
Iteration 9:   log likelihood =  639.38244  
Iteration 10:  log likelihood =  639.41585  
Iteration 11:  log likelihood =  639.41833  
Iteration 12:  log likelihood =   639.4184  
Iteration 13:  log likelihood =   639.4184  

Time-series regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       0.08
Log likelihood =  639.4184                        Prob > chi2     =     0.7762

------------------------------------------------------------------------------
             |                 OPG
 gps_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
gps_returns  |
       _cons |   .0004896   .0011761     0.42   0.677    -.0018154    .0027947
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0215645    .075846     0.28   0.776    -.1270909    .1702199
-------------+----------------------------------------------------------------
HET          |
     bmonday |   1.966533   .5068659     3.88   0.000     .9730939    2.959972
        debt |  -.2054402    .181613    -1.13   0.258    -.5613952    .1505147
   csanction |   .3955628    .194045     2.04   0.041     .0152417    .7758839
      report |   3.911489   .8361058     4.68   0.000     2.272752    5.550226
       _cons |  -8.094501   .0848043   -95.45   0.000    -8.260715   -7.928288
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    31.5187
 Prob > chi2(40)           =     0.8287

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    28.8754
 Prob > chi2(40)           =     0.9042

.           
.           eststo: arch gps_returns, het(bmonday debt csanction report)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  621.30239  
Iteration 1:   log likelihood =  629.18233  
Iteration 2:   log likelihood =  633.63955  
Iteration 3:   log likelihood =  634.95762  
Iteration 4:   log likelihood =  636.60809  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  637.66847  
Iteration 6:   log likelihood =  638.92119  
Iteration 7:   log likelihood =  639.31659  
Iteration 8:   log likelihood =  639.35841  
Iteration 9:   log likelihood =  639.36063  
Iteration 10:  log likelihood =   639.3607  
Iteration 11:  log likelihood =   639.3607  

Time-series regression -- multiplicative heteroskedasticity

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  639.3607                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 gps_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
gps_returns  |
       _cons |   .0004887   .0011487     0.43   0.670    -.0017627    .0027402
-------------+----------------------------------------------------------------
HET          |
     bmonday |   1.951886    .503428     3.88   0.000     .9651855    2.938587
        debt |  -.2125711    .174265    -1.22   0.223    -.5541242     .128982
   csanction |   .3955918   .1929226     2.05   0.040     .0174705    .7737131
      report |    3.91351   .8339313     4.69   0.000     2.279035    5.547985
       _cons |  -8.092062   .0840613   -96.26   0.000    -8.256819   -7.927305
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    31.5084
 Prob > chi2(40)           =     0.8290

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    28.9173
 Prob > chi2(40)           =     0.9032

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant csanction Sanction debt DebtDe
> bate bmonday BlackMonday) nomtitles title(Gap 2011) nodep

Gap 2011
----------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)   
----------------------------------------------------------------------------------------
gps_retu~s                                                                              
Constant      -0.001       -0.001       -0.001        0.000        0.000        0.000   
             (0.002)      (0.002)      (0.002)      (0.001)      (0.001)      (0.001)   
----------------------------------------------------------------------------------------
SIGMA2                                                                                  
Constant       0.001***     0.001***     0.001***                                       
             (0.000)      (0.000)      (0.000)                                          
----------------------------------------------------------------------------------------
ARMA                                                                                    
L.ar                       -0.049       -0.901***    -0.951***     0.022                
                          (0.081)      (0.105)      (0.089)      (0.076)                
L.ma                                     0.827***     0.933***                          
                                       (0.137)      (0.105)                             
----------------------------------------------------------------------------------------
HET                                                                                     
BlackMon~y                                            1.881***     1.967***     1.952***
                                                    (0.486)      (0.507)      (0.503)   
DebtDebate                                           -0.210       -0.205       -0.213   
                                                    (0.177)      (0.182)      (0.174)   
Sanction                                              0.395**      0.396**      0.396** 
                                                    (0.200)      (0.194)      (0.193)   
report                                                3.921***     3.911***     3.914***
                                                    (0.834)      (0.836)      (0.834)   
Constant                                             -8.096***    -8.095***    -8.092***
                                                    (0.086)      (0.085)      (0.084)   
----------------------------------------------------------------------------------------
N                252          252          252          252          252          252   
aic        -1161.264    -1159.869    -1164.219    -1264.296    -1264.837    -1266.721   
bic        -1154.205    -1149.281    -1150.101    -1236.060    -1240.131    -1245.545   
----------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/gps2011.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant cs
> anction Sanction debt DebtDebate bmonday BlackMonday report GapReport) nomtitles title(Gap 2011) nodep replace
(note: file rawtables/gps2011.tex not found)
(output written to rawtables/gps2011.tex)

.           
.   * Apple (1989)
.   
.     * Clear
.           clear

.           
.         * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen aapl_returns = ln(aapl_close/aapl_close[_n-1])
(5,757 missing values generated)

.     
.         * Sanctions
.           gen tsanction3 = 1 if date > td("04jun1989") & date < td("25may1990")
(12,847 missing values generated)

.       recode tsanction3(.=0)
(tsanction3: 12847 changes made)

. 
.         * Bond Market Crash    
.           gen f13minicrash = 1 if date > td("11oct1989") & date < td("17oct1989")
(13,091 missing values generated)

.           recode f13minicrash(.=0)
(f13minicrash: 13091 changes made)

.           
.         * Limit Time Period
.       keep if date > td("30dec1988") & date < td("01jun1990")
(12,737 observations deleted)

.                 
.     * Set for analysis
.           tsset t
        time variable:  t, 5278 to 5634
                delta:  1 unit

.           
.         * Table A.5 Models  
.       eststo clear      

.         
.           eststo: arch aapl_returns, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  841.93383  
Iteration 1:   log likelihood =  843.28002  
Iteration 2:   log likelihood =  843.47478  
Iteration 3:   log likelihood =  843.51332  
Iteration 4:   log likelihood =  843.52072  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  843.52215  
Iteration 6:   log likelihood =   843.5225  
Iteration 7:   log likelihood =  843.52251  

Time-series regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       4.66
Log likelihood =  843.5225                        Prob > chi2     =     0.0308

------------------------------------------------------------------------------
             |                 OPG
aapl_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
aapl_returns |
       _cons |   .0000688   .0013969     0.05   0.961    -.0026691    .0028067
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .1092954   .0506178     2.16   0.031     .0100863    .2085044
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0005191   .0000272    19.11   0.000     .0004658    .0005723
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    28.0474
 Prob > chi2(40)           =     0.9225

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    24.8964
 Prob > chi2(40)           =     0.9705

.           
.           eststo: arch aapl_returns, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  842.12625  
Iteration 1:   log likelihood =  843.49519  
Iteration 2:   log likelihood =  843.67239  
Iteration 3:   log likelihood =   843.7504  
Iteration 4:   log likelihood =  843.76472  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  843.78309  
Iteration 6:   log likelihood =  843.79976  
Iteration 7:   log likelihood =  843.80119  
Iteration 8:   log likelihood =  843.80138  
Iteration 9:   log likelihood =  843.80138  

Time-series regression -- ARMA disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(2)    =       5.31
Log likelihood =  843.8014                        Prob > chi2     =     0.0702

------------------------------------------------------------------------------
             |                 OPG
aapl_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
aapl_returns |
       _cons |   .0000657   .0013803     0.05   0.962    -.0026396    .0027709
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.1485466   .4518396    -0.33   0.742    -1.034136    .7370427
             |
          ma |
         L1. |   .2641849   .4487693     0.59   0.556    -.6153868    1.143757
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0005182   .0000272    19.06   0.000      .000465    .0005715
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    26.1397
 Prob > chi2(40)           =     0.9554

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    24.5978
 Prob > chi2(40)           =     0.9735

.           
.           eststo: arch aapl_returns, ar(1) arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  840.24512  
Iteration 1:   log likelihood =   842.8577  
Iteration 2:   log likelihood =  843.52231  
Iteration 3:   log likelihood =  843.61177  
Iteration 4:   log likelihood =  843.62937  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  843.63306  
Iteration 6:   log likelihood =  843.63434  
Iteration 7:   log likelihood =  843.63438  

ARCH family regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       2.70
Log likelihood =  843.6344                        Prob > chi2     =     0.1004

------------------------------------------------------------------------------
             |                 OPG
aapl_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
aapl_returns |
       _cons |    .000107   .0013884     0.08   0.939    -.0026142    .0028283
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .1031121   .0627651     1.64   0.100    -.0199052    .2261295
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0176532   .0321873     0.55   0.583    -.0454327    .0807391
             |
       _cons |   .0005099   .0000289    17.66   0.000     .0004533    .0005665
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    28.1380
 Prob > chi2(40)           =     0.9206

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    24.9434
 Prob > chi2(40)           =     0.9700

.           
.           eststo: arch aapl_returns, ar(1) arch(1) garch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =   842.0778  
Iteration 1:   log likelihood =  843.43137  
Iteration 2:   log likelihood =  843.65052  
Iteration 3:   log likelihood =  843.74133  
Iteration 4:   log likelihood =   843.7616  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  843.78236  
Iteration 6:   log likelihood =  843.81163  
Iteration 7:   log likelihood =  843.81301  
Iteration 8:   log likelihood =  843.81305  

ARCH family regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       2.19
Log likelihood =   843.813                        Prob > chi2     =     0.1387

------------------------------------------------------------------------------
             |                 OPG
aapl_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
aapl_returns |
       _cons |    .000086    .001379     0.06   0.950    -.0026168    .0027888
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |    .096307   .0650505     1.48   0.139    -.0311896    .2238037
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0279562   .0362854     0.77   0.441    -.0431619    .0990743
             |
       garch |
         L1. |  -.4932796   .8014867    -0.62   0.538    -2.064165    1.077605
             |
       _cons |   .0007585   .0004221     1.80   0.072    -.0000688    .0015858
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    28.1709
 Prob > chi2(40)           =     0.9200

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    25.0583
 Prob > chi2(40)           =     0.9688

.           
.           eststo: arch aapl_returns, ar(1) het(tsanction3 f13minicrash)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  833.68221  
Iteration 1:   log likelihood =  841.32241  
Iteration 2:   log likelihood =  843.26859  
Iteration 3:   log likelihood =  844.25878  
Iteration 4:   log likelihood =  844.54385  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  844.99229  
Iteration 6:   log likelihood =  845.17784  
Iteration 7:   log likelihood =  845.20393  
Iteration 8:   log likelihood =  845.20406  
Iteration 9:   log likelihood =  845.20407  
Iteration 10:  log likelihood =  845.20407  

Time-series regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       4.49
Log likelihood =  845.2041                        Prob > chi2     =     0.0341

------------------------------------------------------------------------------
             |                 OPG
aapl_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
aapl_returns |
       _cons |   .0001929   .0014089     0.14   0.891    -.0025685    .0029544
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |    .112071   .0528881     2.12   0.034     .0084123    .2157297
-------------+----------------------------------------------------------------
HET          |
  tsanction3 |   .1032625   .1031929     1.00   0.317     -.098992    .3055169
f13minicrash |   1.120431   1.076717     1.04   0.298     -.989895    3.230757
       _cons |  -7.653773   .0731179  -104.68   0.000    -7.797081   -7.510464
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    28.5233
 Prob > chi2(40)           =     0.9123

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    23.7905
 Prob > chi2(40)           =     0.9804

.           
.           eststo: arch aapl_returns, het(tsanction3 f13minicrash)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  783.60962  
Iteration 1:   log likelihood =  838.56458  
Iteration 2:   log likelihood =  840.61047  
Iteration 3:   log likelihood =  842.68153  
Iteration 4:   log likelihood =  842.92468  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  842.95718  
Iteration 6:   log likelihood =  842.97133  
Iteration 7:   log likelihood =  842.97292  
Iteration 8:   log likelihood =  842.97316  
Iteration 9:   log likelihood =  842.97317  

Time-series regression -- multiplicative heteroskedasticity

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  842.9732                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
aapl_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
aapl_returns |
       _cons |   .0002132   .0012557     0.17   0.865     -.002248    .0026744
-------------+----------------------------------------------------------------
HET          |
  tsanction3 |   .1380397   .0989007     1.40   0.163    -.0558022    .3318816
f13minicrash |   1.030151   .9638055     1.07   0.285    -.8588733    2.919175
       _cons |  -7.664582   .0728405  -105.22   0.000    -7.807347   -7.521817
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    31.0219
 Prob > chi2(40)           =     0.8448

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    23.4670
 Prob > chi2(40)           =     0.9827

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant tsanction3 Sanction f13minicra
> sh MiniCrash) nomtitles title(Apple 1989) nodep

Apple 1989
----------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)   
----------------------------------------------------------------------------------------
aapl_ret~s                                                                              
Constant       0.000        0.000        0.000        0.000        0.000        0.000   
             (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)   
----------------------------------------------------------------------------------------
ARMA                                                                                    
L.ar           0.109**     -0.149        0.103        0.096        0.112**              
             (0.051)      (0.452)      (0.063)      (0.065)      (0.053)                
L.ma                        0.264                                                       
                          (0.449)                                                       
----------------------------------------------------------------------------------------
SIGMA2                                                                                  
Constant       0.001***     0.001***                                                    
             (0.000)      (0.000)                                                       
----------------------------------------------------------------------------------------
ARCH                                                                                    
L.arch                                   0.018        0.028                             
                                       (0.032)      (0.036)                             
L.garch                                              -0.493                             
                                                    (0.801)                             
Constant                                 0.001***     0.001*                            
                                       (0.000)      (0.000)                             
----------------------------------------------------------------------------------------
HET                                                                                     
Sanction                                                           0.103        0.138   
                                                                 (0.103)      (0.099)   
MiniCrash                                                          1.120        1.030   
                                                                 (1.077)      (0.964)   
Constant                                                          -7.654***    -7.665***
                                                                 (0.073)      (0.073)   
----------------------------------------------------------------------------------------
N                357          357          357          357          357          357   
aic        -1681.045    -1679.603    -1679.269    -1677.626    -1680.408    -1677.946   
bic        -1669.412    -1664.092    -1663.758    -1658.237    -1661.019    -1662.435   
----------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/apple1989.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant 
> tsanction3 Sanction f13minicrash MiniCrash) nomtitles title(Apple 1989) nodep replace
(note: file rawtables/apple1989.tex not found)
(output written to rawtables/apple1989.tex)

.           
.   * Apple (2011)
.   
.     * Clear
.           clear

.           
.         * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen aapl_returns = ln(aapl_close/aapl_close[_n-1])
(5,757 missing values generated)

.     
.     * Currency Sanctions
.       gen csanction = 1 if date > td("11jul2011") & date < td("18oct2011")
(13,025 missing values generated)

.       recode csanction(.=0)
(csanction: 13025 changes made)

. 
.     * Debt Debate
.       gen debt = 1 if date > td("13may2011") & date < td("01aug2011")
(13,041 missing values generated)

.       recode debt(.=0)
(debt: 13041 changes made)

. 
.     * Black Mondayd             
.       gen bmonday = 1 if date > td("01aug2011") & date < td("15aug2011")
(13,085 missing values generated)

.       recode bmonday(.=0)
(bmonday: 13085 changes made)

. 
.     * Limit Time Period
.       keep if date > td("31dec2010") & date < td("03jan2012")   
(12,842 observations deleted)

.                 
.     * Set for analysis
.           tsset t
        time variable:  t, 10825 to 11076
                delta:  1 unit

.           
.         * Table A.6 Models
.           eststo clear

.           
.           eststo: arch aapl_returns, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  675.70664  
Iteration 1:   log likelihood =  676.63644  
Iteration 2:   log likelihood =  676.72198  
Iteration 3:   log likelihood =  676.73319  
Iteration 4:   log likelihood =  676.73521  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  676.73556  
Iteration 6:   log likelihood =  676.73562  

Time-series regression -- AR disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       0.13
Log likelihood =  676.7356                        Prob > chi2     =     0.7200

------------------------------------------------------------------------------
             |                 OPG
aapl_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
aapl_returns |
       _cons |    .000904   .0010967     0.82   0.410    -.0012454    .0030534
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0191497   .0534307     0.36   0.720    -.0855726     .123872
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0002723   .0000199    13.71   0.000     .0002333    .0003112
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    47.7225
 Prob > chi2(40)           =     0.1876

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    46.0636
 Prob > chi2(40)           =     0.2357

.           
.           eststo: arch aapl_returns, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =    675.595  
Iteration 1:   log likelihood =  675.94055  
Iteration 2:   log likelihood =  676.11748  
Iteration 3:   log likelihood =  677.52495  
Iteration 4:   log likelihood =   678.1042  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  678.56964  
Iteration 6:   log likelihood =  678.71847  
Iteration 7:   log likelihood =  678.74657  
Iteration 8:   log likelihood =  678.74859  
Iteration 9:   log likelihood =  678.74862  

Time-series regression -- ARMA disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =     346.37
Log likelihood =  678.7486                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
aapl_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
aapl_returns |
       _cons |   .0009048   .0010147     0.89   0.373    -.0010841    .0028936
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   -.941427   .0702655   -13.40   0.000    -1.079145   -.8037092
             |
          ma |
         L1. |   .8978746   .0922616     9.73   0.000     .7170452    1.078704
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0002679   .0000213    12.56   0.000     .0002261    .0003098
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    43.4302
 Prob > chi2(40)           =     0.3274

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    36.8395
 Prob > chi2(40)           =     0.6133

.           
.           eststo: arch aapl_returns, ar(1) ma(1) arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  675.83881  
Iteration 1:   log likelihood =  675.98352  
Iteration 2:   log likelihood =   678.3146  
Iteration 3:   log likelihood =  679.03948  
Iteration 4:   log likelihood =  680.55481  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  680.95023  
Iteration 6:   log likelihood =  680.98244  
Iteration 7:   log likelihood =  680.99422  
Iteration 8:   log likelihood =  680.99941  
Iteration 9:   log likelihood =  680.99965  
Iteration 10:  log likelihood =  680.99967  

ARCH family regression -- ARMA disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =     402.13
Log likelihood =  680.9997                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
aapl_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
aapl_returns |
       _cons |    .001192   .0010222     1.17   0.244    -.0008115    .0031954
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.9455663   .0668129   -14.15   0.000    -1.076517   -.8146154
             |
          ma |
         L1. |   .9036671   .0920776     9.81   0.000     .7231983    1.084136
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1208955   .0749677     1.61   0.107    -.0260386    .2678296
             |
       _cons |   .0002346   .0000252     9.32   0.000     .0001852     .000284
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    43.2511
 Prob > chi2(40)           =     0.3342

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    33.2739
 Prob > chi2(40)           =     0.7652

.           
.           eststo: arch aapl_returns, ar(1) ma(1) arch(1) garch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  673.48208  
Iteration 1:   log likelihood =  677.28989  
Iteration 2:   log likelihood =  679.20273  
Iteration 3:   log likelihood =  680.10945  
Iteration 4:   log likelihood =  681.02328  
(switching optimization to BFGS)
BFGS stepping has contracted, resetting BFGS Hessian (0)
Iteration 5:   log likelihood =   682.9332  
Iteration 6:   log likelihood =  683.27738  (backed up)
Iteration 7:   log likelihood =  683.32076  (backed up)
Iteration 8:   log likelihood =  683.85064  (backed up)
Iteration 9:   log likelihood =  683.85975  (backed up)
Iteration 10:  log likelihood =  683.94152  
Iteration 11:  log likelihood =  684.45323  
Iteration 12:  log likelihood =  684.66397  
BFGS stepping has contracted, resetting BFGS Hessian (1)
Iteration 13:  log likelihood =  684.72574  
Iteration 14:  log likelihood =  684.72589  (backed up)
(switching optimization to BHHH)
Iteration 15:  log likelihood =  684.72589  (backed up)
Iteration 16:  log likelihood =  684.72846  
Iteration 17:  log likelihood =  684.72889  
Iteration 18:  log likelihood =  684.72889  
Iteration 19:  log likelihood =  684.72974  
(switching optimization to BFGS)
Iteration 20:  log likelihood =   684.7298  
BFGS stepping has contracted, resetting BFGS Hessian (2)
Iteration 21:  log likelihood =  684.72982  
Iteration 22:  log likelihood =  684.72982  (backed up)
Iteration 23:  log likelihood =  684.72982  (backed up)
Iteration 24:  log likelihood =  684.72982  (backed up)
Iteration 25:  log likelihood =  684.72982  (backed up)
Iteration 26:  log likelihood =  684.72982  (backed up)
Iteration 27:  log likelihood =  684.72982  

ARCH family regression -- ARMA disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =       1.65
Log likelihood =  684.7298                        Prob > chi2     =     0.4391

------------------------------------------------------------------------------
             |                 OPG
aapl_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
aapl_returns |
       _cons |   .0010452   .0010376     1.01   0.314    -.0009885    .0030789
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .1813524   .8481796     0.21   0.831    -1.481049    1.843754
             |
          ma |
         L1. |  -.0973887   .8697351    -0.11   0.911    -1.802038    1.607261
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1492249   .0637802     2.34   0.019      .024218    .2742318
             |
       garch |
         L1. |   .7302081    .099598     7.33   0.000     .5349997    .9254166
             |
       _cons |   .0000339   .0000187     1.82   0.069    -2.69e-06    .0000704
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.2565
 Prob > chi2(40)           =     0.5944

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    21.2908
 Prob > chi2(40)           =     0.9933

.           
.           eststo: arch aapl_returns, ar(1) ma(1) arch(1) garch(1) het(bmonday debt csanction)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  682.08404  
Iteration 1:   log likelihood =   683.7036  
Iteration 2:   log likelihood =  683.91913  (backed up)
Iteration 3:   log likelihood =  684.27376  (backed up)
Iteration 4:   log likelihood =  684.62072  (backed up)
(switching optimization to BFGS)
Iteration 5:   log likelihood =  685.77669  
Iteration 6:   log likelihood =  686.52688  (backed up)
Iteration 7:   log likelihood =   687.8517  
Iteration 8:   log likelihood =  688.70831  
Iteration 9:   log likelihood =   689.0044  
Iteration 10:  log likelihood =  689.02382  
Iteration 11:  log likelihood =  689.84114  
Iteration 12:  log likelihood =  689.91689  
Iteration 13:  log likelihood =  690.33206  
Iteration 14:  log likelihood =  690.51968  
(switching optimization to BHHH)
Iteration 15:  log likelihood =  690.65182  
Iteration 16:  log likelihood =  690.76422  
Iteration 17:  log likelihood =  690.82514  
Iteration 18:  log likelihood =   690.8465  
Iteration 19:  log likelihood =  690.85547  
(switching optimization to BFGS)
Iteration 20:  log likelihood =  690.86043  
Iteration 21:  log likelihood =  690.86261  
Iteration 22:  log likelihood =  690.86328  
Iteration 23:  log likelihood =  690.86798  
Iteration 24:  log likelihood =  690.86886  
Iteration 25:  log likelihood =  690.86946  
Iteration 26:  log likelihood =  690.86994  
Iteration 27:  log likelihood =  690.86997  
Iteration 28:  log likelihood =  690.86997  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =       1.25
Log likelihood =    690.87                        Prob > chi2     =     0.5352

------------------------------------------------------------------------------
             |                 OPG
aapl_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
aapl_returns |
       _cons |   .0013922   .0011156     1.25   0.212    -.0007943    .0035788
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .4137923   .8775507     0.47   0.637    -1.306176     2.13376
             |
          ma |
         L1. |  -.3632244    .906983    -0.40   0.689    -2.140878     1.41443
-------------+----------------------------------------------------------------
HET          |
     bmonday |   .6383364   .2835026     2.25   0.024     .0826814    1.193991
        debt |  -.0434008   .2544247    -0.17   0.865    -.5420641    .4552625
   csanction |   .6344396    .220857     2.87   0.004     .2015679    1.067311
       _cons |  -7.979997   .1649324   -48.38   0.000    -8.303259   -7.656736
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0782545   .0461926     1.69   0.090    -.0122812    .1687903
             |
       garch |
         L1. |  -.7191912    .154112    -4.67   0.000    -1.021245   -.4171371
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    36.7056
 Prob > chi2(40)           =     0.6194

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    30.1518
 Prob > chi2(40)           =     0.8709

.           
.       esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant csanction Sanction debt DebtDebate
>  bmonday BlackMonday) nomtitles title(Apple 2011) nodep

Apple 2011
---------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)   
---------------------------------------------------------------------------
aapl_ret~s                                                                 
Constant       0.001        0.001        0.001        0.001        0.001   
             (0.001)      (0.001)      (0.001)      (0.001)      (0.001)   
---------------------------------------------------------------------------
ARMA                                                                       
L.ar           0.019       -0.941***    -0.946***     0.181        0.414   
             (0.053)      (0.070)      (0.067)      (0.848)      (0.878)   
L.ma                        0.898***     0.904***    -0.097       -0.363   
                          (0.092)      (0.092)      (0.870)      (0.907)   
---------------------------------------------------------------------------
SIGMA2                                                                     
Constant       0.000***     0.000***                                       
             (0.000)      (0.000)                                          
---------------------------------------------------------------------------
ARCH                                                                       
L.arch                                   0.121        0.149**      0.078*  
                                       (0.075)      (0.064)      (0.046)   
L.garch                                               0.730***    -0.719***
                                                    (0.100)      (0.154)   
Constant                                 0.000***     0.000*               
                                       (0.000)      (0.000)                
---------------------------------------------------------------------------
HET                                                                        
BlackMon~y                                                         0.638** 
                                                                 (0.284)   
DebtDebate                                                        -0.043   
                                                                 (0.254)   
Sanction                                                           0.634***
                                                                 (0.221)   
Constant                                                          -7.980***
                                                                 (0.165)   
---------------------------------------------------------------------------
N                252          252          252          252          252   
aic        -1347.471    -1349.497    -1351.999    -1357.460    -1363.740   
bic        -1336.883    -1335.380    -1334.352    -1336.283    -1331.975   
---------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.       esttab using rawtables/apple2011.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant csan
> ction Sanction debt DebtDebate bmonday BlackMonday) nomtitles title(Apple 2011) nodep  replace  
(note: file rawtables/apple2011.tex not found)
(output written to rawtables/apple2011.tex)

.   
.   * Dell (1989) 
.   
.     * Clear
.           clear

.           
.         * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen dell_returns = ln(dell_close/dell_close[_n-1])
(6,704 missing values generated)

.     
.         * Sanctions
.           gen tsanction3 = 1 if date > td("04jun1989") & date < td("25may1990")
(12,847 missing values generated)

.       recode tsanction3(.=0)
(tsanction3: 12847 changes made)

. 
.         * Bond Market Crash    
.           gen f13minicrash = 1 if date > td("11oct1989") & date < td("17oct1989")
(13,091 missing values generated)

.           recode f13minicrash(.=0)
(f13minicrash: 13091 changes made)

.           
.         * Limit Time Period
.       keep if date > td("30dec1988") & date < td("01jun1990")
(12,737 observations deleted)

.                 
.     * Set for analysis
.           tsset t
        time variable:  t, 5278 to 5634
                delta:  1 unit

.           
.         * Table A.7 Models  
.       eststo clear      

.           
.           eststo: arch dell_returns, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  679.18616  
Iteration 1:   log likelihood =   680.6344  
Iteration 2:   log likelihood =  680.79281  
Iteration 3:   log likelihood =  680.84535  
Iteration 4:   log likelihood =  680.85845  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  680.86292  
Iteration 6:   log likelihood =  680.86521  
Iteration 7:   log likelihood =  680.86526  

Time-series regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       0.30
Log likelihood =  680.8653                        Prob > chi2     =     0.5808

------------------------------------------------------------------------------
             |                 OPG
dell_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dell_returns |
       _cons |   .0004315   .0020208     0.21   0.831    -.0035292    .0043922
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0189335   .0342837     0.55   0.581    -.0482614    .0861284
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0012911    .000057    22.67   0.000     .0011795    .0014028
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.0152
 Prob > chi2(40)           =     0.6054

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    63.7725
 Prob > chi2(40)           =     0.0098

.           
.           eststo: arch dell_returns, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  679.16263  
Iteration 1:   log likelihood =  680.53699  
Iteration 2:   log likelihood =  680.62453  
Iteration 3:   log likelihood =    680.756  
Iteration 4:   log likelihood =  680.80916  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  680.81369  
Iteration 6:   log likelihood =  680.84595  
Iteration 7:   log likelihood =  680.86543  
Iteration 8:   log likelihood =  680.86675  
Iteration 9:   log likelihood =  680.86684  
Iteration 10:  log likelihood =  680.86689  
Iteration 11:  log likelihood =  680.86691  

Time-series regression -- ARMA disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(2)    =       0.33
Log likelihood =  680.8669                        Prob > chi2     =     0.8463

------------------------------------------------------------------------------
             |                 OPG
dell_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dell_returns |
       _cons |    .000432   .0020966     0.21   0.837    -.0036772    .0045412
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0748186   2.021922     0.04   0.970    -3.888076    4.037713
             |
          ma |
         L1. |  -.0556958   2.034111    -0.03   0.978    -4.042481    3.931089
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0012911   .0000573    22.51   0.000     .0011787    .0014035
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    36.9918
 Prob > chi2(40)           =     0.6064

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    63.7200
 Prob > chi2(40)           =     0.0099

.           
.           eststo: arch dell_returns, ar(1) ma(1) arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  699.93982  
Iteration 1:   log likelihood =  701.87471  
Iteration 2:   log likelihood =  702.21582  
Iteration 3:   log likelihood =  702.45947  
Iteration 4:   log likelihood =   702.5229  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  702.61921  
Iteration 6:   log likelihood =  702.78207  
Iteration 7:   log likelihood =  702.89616  
Iteration 8:   log likelihood =   702.9049  
Iteration 9:   log likelihood =  702.90525  
Iteration 10:  log likelihood =   702.9053  
Iteration 11:  log likelihood =  702.90531  

ARCH family regression -- ARMA disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(2)    =       2.18
Log likelihood =  702.9053                        Prob > chi2     =     0.3364

------------------------------------------------------------------------------
             |                 OPG
dell_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dell_returns |
       _cons |  -.0015124   .0018753    -0.81   0.420    -.0051879    .0021632
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0648338   .5541001     0.12   0.907    -1.021182     1.15085
             |
          ma |
         L1. |   .0241182   .5473178     0.04   0.965    -1.048605    1.096841
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .5197802   .1039859     5.00   0.000     .3159715    .7235888
             |
       _cons |    .000755   .0000547    13.80   0.000     .0006478    .0008622
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    44.3471
 Prob > chi2(40)           =     0.2934

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    17.0306
 Prob > chi2(40)           =     0.9995

.           
.           eststo: arch dell_returns, ar(1) arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  699.84884  
Iteration 1:   log likelihood =   701.8019  
Iteration 2:   log likelihood =  702.29945  
Iteration 3:   log likelihood =  702.56002  
Iteration 4:   log likelihood =  702.70335  
(switching optimization to BFGS)
Iteration 5:   log likelihood =   702.7868  
Iteration 6:   log likelihood =   702.9002  
Iteration 7:   log likelihood =  702.90338  
Iteration 8:   log likelihood =  702.90346  
Iteration 9:   log likelihood =  702.90349  

ARCH family regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       2.18
Log likelihood =  702.9035                        Prob > chi2     =     0.1400

------------------------------------------------------------------------------
             |                 OPG
dell_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dell_returns |
       _cons |   -.001513   .0018457    -0.82   0.412    -.0051305    .0021045
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0892176   .0604492     1.48   0.140    -.0292606    .2076959
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .5200871    .095352     5.45   0.000     .3332005    .7069736
             |
       _cons |   .0007548   .0000544    13.88   0.000     .0006482    .0008613
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    44.3424
 Prob > chi2(40)           =     0.2936

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    17.0641
 Prob > chi2(40)           =     0.9994

.           
.           eststo: arch dell_returns, arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  699.33786  
Iteration 1:   log likelihood =  701.35418  
Iteration 2:   log likelihood =  701.88599  
Iteration 3:   log likelihood =  701.95235  
Iteration 4:   log likelihood =   701.9662  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  701.96736  
Iteration 6:   log likelihood =  701.96747  
Iteration 7:   log likelihood =  701.96748  

ARCH family regression

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  701.9675                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
dell_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dell_returns |
       _cons |  -.0016066   .0015851    -1.01   0.311    -.0047133    .0015002
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .4706863   .0907193     5.19   0.000     .2928798    .6484928
             |
       _cons |   .0007917    .000055    14.38   0.000     .0006839    .0008996
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.2338
 Prob > chi2(40)           =     0.4599

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    15.3053
 Prob > chi2(40)           =     0.9999

.           
.           eststo: arch dell_returns, arch(1) garch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  699.64629  
Iteration 1:   log likelihood =  701.73206  
Iteration 2:   log likelihood =  702.62254  
Iteration 3:   log likelihood =  702.84091  
Iteration 4:   log likelihood =  702.90623  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  702.92598  
Iteration 6:   log likelihood =  702.93478  
Iteration 7:   log likelihood =  702.93509  
Iteration 8:   log likelihood =  702.93515  

ARCH family regression

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  702.9351                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
dell_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dell_returns |
       _cons |  -.0020066   .0017943    -1.12   0.263    -.0055234    .0015103
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .4397136   .0941336     4.67   0.000     .2552153     .624212
             |
       garch |
         L1. |   .1093566   .0788567     1.39   0.166    -.0451997    .2639128
             |
       _cons |   .0006704   .0000828     8.10   0.000     .0005081    .0008327
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.1921
 Prob > chi2(40)           =     0.4617

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    15.0443
 Prob > chi2(40)           =     0.9999

.           
.           eststo: arch dell_returns, arch(1) het(tsanction3 f13minicrash)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  568.83983  
Iteration 1:   log likelihood =  673.15075  
Iteration 2:   log likelihood =  699.58705  
Iteration 3:   log likelihood =  700.86237  
Iteration 4:   log likelihood =  701.11101  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  701.44825  
Iteration 6:   log likelihood =  702.00429  
Iteration 7:   log likelihood =  702.04121  
Iteration 8:   log likelihood =  702.04318  
Iteration 9:   log likelihood =  702.04378  
Iteration 10:  log likelihood =  702.04381  

ARCH family regression -- multiplicative heteroskedasticity

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  702.0438                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
dell_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dell_returns |
       _cons |  -.0016602   .0016768    -0.99   0.322    -.0049467    .0016262
-------------+----------------------------------------------------------------
HET          |
  tsanction3 |  -.0628226   .1424107    -0.44   0.659    -.3419425    .2162972
f13minicrash |  -.2885958   2.181183    -0.13   0.895    -4.563635    3.986443
       _cons |  -7.081931   .1440765   -49.15   0.000    -7.364316   -6.799546
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .4504144   .0962681     4.68   0.000     .2617323    .6390964
------------------------------------------------------------------------------
(est7 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    39.8360
 Prob > chi2(40)           =     0.4776

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    15.2623
 Prob > chi2(40)           =     0.9999

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant tsanction3 Sanction f13minicra
> sh MiniCrash) nomtitles title(Dell 1989) nodep

Dell 1989
-----------------------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)          (7)   
-----------------------------------------------------------------------------------------------------
dell_ret~s                                                                                           
Constant       0.000        0.000       -0.002       -0.002       -0.002       -0.002       -0.002   
             (0.002)      (0.002)      (0.002)      (0.002)      (0.002)      (0.002)      (0.002)   
-----------------------------------------------------------------------------------------------------
ARMA                                                                                                 
L.ar           0.019        0.075        0.065        0.089                                          
             (0.034)      (2.022)      (0.554)      (0.060)                                          
L.ma                       -0.056        0.024                                                       
                          (2.034)      (0.547)                                                       
-----------------------------------------------------------------------------------------------------
SIGMA2                                                                                               
Constant       0.001***     0.001***                                                                 
             (0.000)      (0.000)                                                                    
-----------------------------------------------------------------------------------------------------
ARCH                                                                                                 
L.arch                                   0.520***     0.520***     0.471***     0.440***     0.450***
                                       (0.104)      (0.095)      (0.091)      (0.094)      (0.096)   
L.garch                                                                         0.109                
                                                                              (0.079)                
Constant                                 0.001***     0.001***     0.001***     0.001***             
                                       (0.000)      (0.000)      (0.000)      (0.000)                
-----------------------------------------------------------------------------------------------------
HET                                                                                                  
Sanction                                                                                    -0.063   
                                                                                           (0.142)   
MiniCrash                                                                                   -0.289   
                                                                                           (2.181)   
Constant                                                                                    -7.082***
                                                                                           (0.144)   
-----------------------------------------------------------------------------------------------------
N                357          357          357          357          357          357          357   
aic        -1355.731    -1353.734    -1395.811    -1397.807    -1397.935    -1397.870    -1394.088   
bic        -1344.097    -1338.223    -1376.422    -1382.296    -1386.302    -1382.359    -1374.699   
-----------------------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/dell1989.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant t
> sanction3 Sanction f13minicrash MiniCrash) nomtitles title(Dell 1989) nodep replace
(note: file rawtables/dell1989.tex not found)
(output written to rawtables/dell1989.tex)

.           
.   * Dell (2011)
.   
.     * Clear
.           clear

.           
.         * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen dell_returns = ln(dell_close/dell_close[_n-1])
(6,704 missing values generated)

.     
.     * Currency Sanctions
.       gen csanction = 1 if date > td("11jul2011") & date < td("18oct2011")
(13,025 missing values generated)

.       recode csanction(.=0)
(csanction: 13025 changes made)

. 
.     * Debt Debate
.       gen debt = 1 if date > td("13may2011") & date < td("01aug2011")
(13,041 missing values generated)

.       recode debt(.=0)
(debt: 13041 changes made)

. 
.     * Black Mondayd             
.       gen bmonday = 1 if date > td("01aug2011") & date < td("15aug2011")
(13,085 missing values generated)

.       recode bmonday(.=0)
(bmonday: 13085 changes made)

. 
.     * Limit Time Period
.       keep if date > td("31dec2010") & date < td("03jan2012")   
(12,842 observations deleted)

.                 
.     * Set for analysis
.           tsset t
        time variable:  t, 10825 to 11076
                delta:  1 unit

.           
.         * Table A.8 Models
.           eststo clear

.           
.           eststo: arch dell_returns, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  585.34883  
Iteration 1:   log likelihood =   585.5712  
Iteration 2:   log likelihood =  585.61359  
Iteration 3:   log likelihood =  585.61436  
Iteration 4:   log likelihood =  585.61454  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  585.61459  
Iteration 6:   log likelihood =   585.6146  

Time-series regression -- AR disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       1.30
Log likelihood =  585.6146                        Prob > chi2     =     0.2548

------------------------------------------------------------------------------
             |                 OPG
dell_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dell_returns |
       _cons |   .0003103   .0013977     0.22   0.824    -.0024291    .0030497
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.0707803   .0621579    -1.14   0.255    -.1926076     .051047
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0005611   .0000318    17.62   0.000     .0004987    .0006235
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    46.2322
 Prob > chi2(40)           =     0.2305

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    28.5744
 Prob > chi2(40)           =     0.9112

.           
.           eststo: arch dell_returns, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  585.44451  
Iteration 1:   log likelihood =  587.11716  
Iteration 2:   log likelihood =  588.35516  
Iteration 3:   log likelihood =  588.58886  
Iteration 4:   log likelihood =  588.64805  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  588.66807  
Iteration 6:   log likelihood =  588.67447  
Iteration 7:   log likelihood =  588.67529  
Iteration 8:   log likelihood =  588.67544  
Iteration 9:   log likelihood =  588.67545  

Time-series regression -- ARMA disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =     802.31
Log likelihood =  588.6755                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
dell_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dell_returns |
       _cons |   .0003573   .0005518     0.65   0.517    -.0007243    .0014388
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |    .870315    .052746    16.50   0.000     .7669347    .9736953
             |
          ma |
         L1. |  -.9556575   .0365304   -26.16   0.000    -1.027256   -.8840591
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0005476    .000029    18.86   0.000     .0004907    .0006046
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    38.1589
 Prob > chi2(40)           =     0.5534

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    27.5009
 Prob > chi2(40)           =     0.9332

.           
.           eststo: arch dell_returns, ar(1) ma(1) arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  585.57824  
Iteration 1:   log likelihood =  587.06294  
Iteration 2:   log likelihood =  588.04319  
Iteration 3:   log likelihood =   588.5471  
Iteration 4:   log likelihood =  588.61109  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  588.65292  
Iteration 6:   log likelihood =  588.65984  
Iteration 7:   log likelihood =  588.67424  
Iteration 8:   log likelihood =  588.67635  
Iteration 9:   log likelihood =  588.67654  
Iteration 10:  log likelihood =  588.67661  
Iteration 11:  log likelihood =  588.67661  

ARCH family regression -- ARMA disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =     803.38
Log likelihood =  588.6766                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
dell_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dell_returns |
       _cons |   .0003586   .0005513     0.65   0.515    -.0007219    .0014391
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .8705816   .0534545    16.29   0.000     .7658128    .9753505
             |
          ma |
         L1. |  -.9557672   .0365697   -26.14   0.000    -1.027442   -.8840919
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0014121   .0312398     0.05   0.964    -.0598168    .0626409
             |
       _cons |   .0005469   .0000312    17.53   0.000     .0004857     .000608
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    38.0993
 Prob > chi2(40)           =     0.5561

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    27.4307
 Prob > chi2(40)           =     0.9345

.           
.           eststo: arch dell_returns, ar(1) arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  585.46219  
Iteration 1:   log likelihood =  585.63391  
Iteration 2:   log likelihood =  585.66218  
Iteration 3:   log likelihood =  585.67147  
Iteration 4:   log likelihood =  585.67342  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  585.67388  
Iteration 6:   log likelihood =  585.67397  
Iteration 7:   log likelihood =  585.67397  

ARCH family regression -- AR disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       1.16
Log likelihood =   585.674                        Prob > chi2     =     0.2815

------------------------------------------------------------------------------
             |                 OPG
dell_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dell_returns |
       _cons |   .0003211    .001383     0.23   0.816    -.0023894    .0030317
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.0722016   .0670414    -1.08   0.281    -.2036004    .0591972
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0143307   .0442781     0.32   0.746    -.0724527    .1011141
             |
       _cons |    .000553   .0000368    15.04   0.000     .0004809    .0006251
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    45.6514
 Prob > chi2(40)           =     0.2489

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    27.5505
 Prob > chi2(40)           =     0.9323

.           
.           eststo: arch dell_returns, arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  585.06024  
Iteration 1:   log likelihood =  585.06332  
Iteration 2:   log likelihood =  585.06366  
Iteration 3:   log likelihood =  585.06372  
Iteration 4:   log likelihood =  585.06373  

ARCH family regression

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  585.0637                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
dell_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dell_returns |
       _cons |   .0003261   .0014882     0.22   0.827    -.0025907    .0032429
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0183319   .0447537     0.41   0.682    -.0693837    .1060475
             |
       _cons |   .0005537   .0000352    15.73   0.000     .0004847    .0006227
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    44.0982
 Prob > chi2(40)           =     0.3024

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    27.6848
 Prob > chi2(40)           =     0.9298

.           
.           eststo: arch dell_returns, arch(1) garch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  584.10414  
Iteration 1:   log likelihood =  585.73692  
Iteration 2:   log likelihood =  585.86922  
Iteration 3:   log likelihood =  587.59586  
Iteration 4:   log likelihood =  588.75226  
(switching optimization to BFGS)
Iteration 5:   log likelihood =   589.9579  
Iteration 6:   log likelihood =  590.36477  
Iteration 7:   log likelihood =  590.64321  
Iteration 8:   log likelihood =  591.12964  
Iteration 9:   log likelihood =  591.30678  
Iteration 10:  log likelihood =  591.32985  
Iteration 11:  log likelihood =  591.33327  
Iteration 12:  log likelihood =  591.33381  
Iteration 13:  log likelihood =  591.33394  
Iteration 14:  log likelihood =  591.33394  

ARCH family regression

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  591.3339                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
dell_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dell_returns |
       _cons |   .0006286    .001633     0.38   0.700    -.0025721    .0038292
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0780125   .0513146     1.52   0.128    -.0225623    .1785874
             |
       garch |
         L1. |   .7802269    .153534     5.08   0.000     .4793059    1.081148
             |
       _cons |   .0000797   .0000596     1.34   0.181     -.000037    .0001965
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    42.5991
 Prob > chi2(40)           =     0.3599

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    11.7930
 Prob > chi2(40)           =     1.0000

.           
.           eststo: arch dell_returns, arch(1) garch(1) het(bmonday debt csanction)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  591.54888  
Iteration 1:   log likelihood =  593.16825  
Iteration 2:   log likelihood =   593.8559  
Iteration 3:   log likelihood =   595.6883  
Iteration 4:   log likelihood =  608.93648  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  611.66246  
Iteration 6:   log likelihood =  614.15282  
Iteration 7:   log likelihood =  615.12271  
Iteration 8:   log likelihood =  615.89813  
Iteration 9:   log likelihood =   616.1624  
Iteration 10:  log likelihood =   617.0882  
Iteration 11:  log likelihood =  617.52623  
Iteration 12:  log likelihood =   617.7854  
Iteration 13:  log likelihood =   618.1193  
Iteration 14:  log likelihood =  618.20025  
(switching optimization to BHHH)
Iteration 15:  log likelihood =  618.64521  
Iteration 16:  log likelihood =   618.7408  
Iteration 17:  log likelihood =  618.82398  
Iteration 18:  log likelihood =  618.86028  
Iteration 19:  log likelihood =  618.94317  
(switching optimization to BFGS)
Iteration 20:  log likelihood =  619.01529  
Iteration 21:  log likelihood =   619.5818  
Iteration 22:  log likelihood =  619.58332  (backed up)
Iteration 23:  log likelihood =  619.61085  
Iteration 24:  log likelihood =  619.62498  
Iteration 25:  log likelihood =  619.63019  
Iteration 26:  log likelihood =  619.66383  
Iteration 27:  log likelihood =  619.68691  
Iteration 28:  log likelihood =  619.70766  
Iteration 29:  log likelihood =  619.72408  
(switching optimization to BHHH)
Iteration 30:  log likelihood =  619.72555  
Iteration 31:  log likelihood =  619.73721  
Iteration 32:  log likelihood =  619.74203  
Iteration 33:  log likelihood =  619.75414  
Iteration 34:  log likelihood =  619.76552  
(switching optimization to BFGS)
Iteration 35:  log likelihood =  619.77621  
Iteration 36:  log likelihood =  619.78664  
Iteration 37:  log likelihood =  619.83413  
Iteration 38:  log likelihood =  619.93629  
Iteration 39:  log likelihood =  619.95229  
Iteration 40:  log likelihood =  619.97348  
Iteration 41:  log likelihood =  619.99263  
Iteration 42:  log likelihood =  620.02157  
Iteration 43:  log likelihood =  620.02573  
Iteration 44:  log likelihood =  620.03075  
(switching optimization to BHHH)
Iteration 45:  log likelihood =   620.0361  
Iteration 46:  log likelihood =  620.04225  
Iteration 47:  log likelihood =  620.04484  (backed up)
Iteration 48:  log likelihood =  620.04703  (backed up)
Iteration 49:  log likelihood =  620.04887  (backed up)
(switching optimization to BFGS)
Iteration 50:  log likelihood =  620.05042  (backed up)
Iteration 51:  log likelihood =  620.05249  
Iteration 52:  log likelihood =  620.07803  
Iteration 53:  log likelihood =  620.07853  
BFGS stepping has contracted, resetting BFGS Hessian (0)
Iteration 54:  log likelihood =  620.08711  
Iteration 55:  log likelihood =  620.08874  (backed up)
Iteration 56:  log likelihood =  620.08969  (backed up)
Iteration 57:  log likelihood =  620.09167  (backed up)
Iteration 58:  log likelihood =    620.093  (backed up)
Iteration 59:  log likelihood =  620.09326  (backed up)
(switching optimization to BHHH)
Iteration 60:  log likelihood =  620.09374  
Iteration 61:  log likelihood =  620.09481  (backed up)
Iteration 62:  log likelihood =  620.09558  (backed up)
Iteration 63:  log likelihood =  620.09611  (backed up)
Iteration 64:  log likelihood =  620.09644  (backed up)
(switching optimization to BFGS)
Iteration 65:  log likelihood =   620.0966  (backed up)
Iteration 66:  log likelihood =  620.09665  
Iteration 67:  log likelihood =  620.12126  
Iteration 68:  log likelihood =  620.12188  
Iteration 69:  log likelihood =  620.12385  
Iteration 70:  log likelihood =  620.13156  
Iteration 71:  log likelihood =  620.13227  
BFGS stepping has contracted, resetting BFGS Hessian (1)
Iteration 72:  log likelihood =  620.13433  
Iteration 73:  log likelihood =  620.13443  (backed up)
Iteration 74:  log likelihood =  620.13605  (backed up)
(switching optimization to BHHH)
Iteration 75:  log likelihood =  620.13901  (backed up)
Iteration 76:  log likelihood =  620.14107  
Iteration 77:  log likelihood =  620.14179  (backed up)
Iteration 78:  log likelihood =  620.14211  (backed up)
Iteration 79:  log likelihood =  620.14214  (backed up)
(switching optimization to BFGS)
Iteration 80:  log likelihood =   620.1429  (backed up)
Iteration 81:  log likelihood =  620.14498  
Iteration 82:  log likelihood =  620.16717  
Iteration 83:  log likelihood =  620.16803  (backed up)
Iteration 84:  log likelihood =  620.17155  
BFGS stepping has contracted, resetting BFGS Hessian (2)
Iteration 85:  log likelihood =  620.17418  
Iteration 86:  log likelihood =   620.1751  (backed up)
Iteration 87:  log likelihood =  620.17615  (backed up)
Iteration 88:  log likelihood =  620.17873  (backed up)
Iteration 89:  log likelihood =  620.17919  (backed up)
(switching optimization to BHHH)
Iteration 90:  log likelihood =  620.17954  (backed up)
Iteration 91:  log likelihood =  620.18008  (backed up)
Iteration 92:  log likelihood =  620.18022  (backed up)
Iteration 93:  log likelihood =  620.18079  (backed up)
Iteration 94:  log likelihood =  620.18133  (backed up)
(switching optimization to BFGS)
Iteration 95:  log likelihood =  620.18186  (backed up)
Iteration 96:  log likelihood =  620.18238  
Iteration 97:  log likelihood =  620.19194  
Iteration 98:  log likelihood =  620.19198  
BFGS stepping has contracted, resetting BFGS Hessian (3)
Iteration 99:  log likelihood =   620.1946  
Iteration 100: log likelihood =  620.19491  (backed up)
Iteration 101: log likelihood =  620.19505  (backed up)
Iteration 102: log likelihood =  620.19567  (backed up)
Iteration 103: log likelihood =  620.19667  (backed up)
Iteration 104: log likelihood =  620.19698  (backed up)
(switching optimization to BHHH)
Iteration 105: log likelihood =  620.19709  
Iteration 106: log likelihood =  620.19766  (backed up)
Iteration 107: log likelihood =  620.19785  (backed up)
Iteration 108: log likelihood =  620.19834  (backed up)
Iteration 109: log likelihood =  620.19882  (backed up)
(switching optimization to BFGS)
Iteration 110: log likelihood =  620.19929  (backed up)
Iteration 111: log likelihood =  620.19978  
Iteration 112: log likelihood =  620.20745  
Iteration 113: log likelihood =  620.20835  
Iteration 114: log likelihood =  620.21143  
Iteration 115: log likelihood =  620.21184  
BFGS stepping has contracted, resetting BFGS Hessian (4)
Iteration 116: log likelihood =  620.21417  
Iteration 117: log likelihood =  620.21427  (backed up)
Iteration 118: log likelihood =  620.21457  (backed up)
Iteration 119: log likelihood =  620.21691  (backed up)
(switching optimization to BHHH)
Iteration 120: log likelihood =  620.21713  (backed up)
Iteration 121: log likelihood =  620.21844  (backed up)
Iteration 122: log likelihood =  620.21933  (backed up)
Iteration 123: log likelihood =  620.21988  (backed up)
Iteration 124: log likelihood =  620.22016  (backed up)
(switching optimization to BFGS)
Iteration 125: log likelihood =  620.22022  (backed up)
Iteration 126: log likelihood =  620.22085  
BFGS stepping has contracted, resetting BFGS Hessian (5)
Iteration 127: log likelihood =  620.23047  
Iteration 128: log likelihood =  620.23053  (backed up)
Iteration 129: log likelihood =  620.23056  (backed up)
Iteration 130: log likelihood =  620.23086  (backed up)
Iteration 131: log likelihood =  620.23354  (backed up)
Iteration 132: log likelihood =  620.23402  (backed up)
Iteration 133: log likelihood =  620.23414  (backed up)
Iteration 134: log likelihood =  620.23416  
(switching optimization to BHHH)
Iteration 135: log likelihood =  620.23416  
Iteration 136: log likelihood =  620.23474  (backed up)
Iteration 137: log likelihood =  620.23495  (backed up)
Iteration 138: log likelihood =   620.2353  (backed up)
Iteration 139: log likelihood =  620.23563  (backed up)
(switching optimization to BFGS)
Iteration 140: log likelihood =  620.23594  (backed up)
Iteration 141: log likelihood =  620.23627  
Iteration 142: log likelihood =  620.24004  
Iteration 143: log likelihood =  620.24005  (backed up)
Iteration 144: log likelihood =  620.24118  
Iteration 145: log likelihood =  620.24162  
BFGS stepping has contracted, resetting BFGS Hessian (6)
Iteration 146: log likelihood =  620.24332  
Iteration 147: log likelihood =  620.24334  (backed up)
Iteration 148: log likelihood =  620.24354  (backed up)
Iteration 149: log likelihood =  620.24355  (backed up)
(switching optimization to BHHH)
Iteration 150: log likelihood =   620.2436  (backed up)
Iteration 151: log likelihood =  620.24429  
Iteration 152: log likelihood =   620.2446  (backed up)
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Iteration 154: log likelihood =  620.24517  (backed up)
(switching optimization to BFGS)
Iteration 155: log likelihood =  620.24544  (backed up)
Iteration 156: log likelihood =  620.24547  
Iteration 157: log likelihood =  620.24678  
Iteration 158: log likelihood =  620.24806  
Iteration 159: log likelihood =  620.25008  
BFGS stepping has contracted, resetting BFGS Hessian (7)
Iteration 160: log likelihood =  620.25085  
Iteration 161: log likelihood =  620.25085  (backed up)
Iteration 162: log likelihood =   620.2509  (backed up)
Iteration 163: log likelihood =  620.25099  (backed up)
Iteration 164: log likelihood =  620.25118  (backed up)
(switching optimization to BHHH)
Iteration 165: log likelihood =  620.25127  (backed up)
Iteration 166: log likelihood =   620.2513  (backed up)
Iteration 167: log likelihood =  620.25154  (backed up)
Iteration 168: log likelihood =  620.25177  (backed up)
Iteration 169: log likelihood =  620.25198  (backed up)
(switching optimization to BFGS)
Iteration 170: log likelihood =  620.25219  (backed up)
Iteration 171: log likelihood =  620.25239  
Iteration 172: log likelihood =  620.25652  
Iteration 173: log likelihood =  620.25672  
BFGS stepping has contracted, resetting BFGS Hessian (8)
Iteration 174: log likelihood =  620.25766  
Iteration 175: log likelihood =  620.25804  (backed up)
Iteration 176: log likelihood =  620.25824  (backed up)
Iteration 177: log likelihood =  620.25837  (backed up)
Iteration 178: log likelihood =  620.25856  (backed up)
Iteration 179: log likelihood =  620.25881  (backed up)
(switching optimization to BHHH)
Iteration 180: log likelihood =  620.25886  
Iteration 181: log likelihood =  620.25904  (backed up)
Iteration 182: log likelihood =  620.25928  (backed up)
Iteration 183: log likelihood =  620.25951  (backed up)
Iteration 184: log likelihood =  620.25973  (backed up)
(switching optimization to BFGS)
Iteration 185: log likelihood =  620.25994  (backed up)
Iteration 186: log likelihood =  620.26016  
Iteration 187: log likelihood =  620.26292  
Iteration 188: log likelihood =   620.2632  
BFGS stepping has contracted, resetting BFGS Hessian (9)
Iteration 189: log likelihood =  620.26362  
Iteration 190: log likelihood =   620.2639  (backed up)
Iteration 191: log likelihood =   620.2639  (backed up)
Iteration 192: log likelihood =    620.264  (backed up)
Iteration 193: log likelihood =  620.26436  (backed up)
Iteration 194: log likelihood =  620.26478  (backed up)
(switching optimization to BHHH)
Iteration 195: log likelihood =  620.26483  
Iteration 196: log likelihood =   620.2652  (backed up)
Iteration 197: log likelihood =  620.26538  (backed up)
Iteration 198: log likelihood =   620.2654  (backed up)
Iteration 199: log likelihood =  620.26558  (backed up)
(switching optimization to BFGS)
Iteration 200: log likelihood =  620.26575  (backed up)
Iteration 201: log likelihood =  620.26598  
BFGS stepping has contracted, resetting BFGS Hessian (10)
Iteration 202: log likelihood =  620.26866  
Iteration 203: log likelihood =  620.26868  (backed up)
Iteration 204: log likelihood =  620.26882  (backed up)
Iteration 205: log likelihood =  620.26903  (backed up)
Iteration 206: log likelihood =  620.26911  (backed up)
Iteration 207: log likelihood =   620.2692  (backed up)
Iteration 208: log likelihood =  620.26926  
Iteration 209: log likelihood =  620.26936  
(switching optimization to BHHH)
Iteration 210: log likelihood =  620.26937  
Iteration 211: log likelihood =   620.2701  
Iteration 212: log likelihood =  620.27023  (backed up)
Iteration 213: log likelihood =  620.27025  (backed up)
Iteration 214: log likelihood =  620.27041  (backed up)
(switching optimization to BFGS)
Iteration 215: log likelihood =  620.27056  (backed up)
Iteration 216: log likelihood =  620.27078  
Iteration 217: log likelihood =  620.27337  
Iteration 218: log likelihood =  620.27358  
Iteration 219: log likelihood =  620.27362  
Iteration 220: log likelihood =  620.27392  
BFGS stepping has contracted, resetting BFGS Hessian (11)
Iteration 221: log likelihood =  620.27437  
Iteration 222: log likelihood =  620.27454  (backed up)
Iteration 223: log likelihood =  620.27459  (backed up)
Iteration 224: log likelihood =  620.27459  (backed up)
(switching optimization to BHHH)
Iteration 225: log likelihood =  620.27475  (backed up)
Iteration 226: log likelihood =  620.27501  (backed up)
Iteration 227: log likelihood =  620.27515  (backed up)
Iteration 228: log likelihood =   620.2752  (backed up)
Iteration 229: log likelihood =  620.27534  (backed up)
(switching optimization to BFGS)
Iteration 230: log likelihood =  620.27548  (backed up)
Iteration 231: log likelihood =  620.27567  
BFGS stepping has contracted, resetting BFGS Hessian (12)
Iteration 232: log likelihood =  620.27796  
Iteration 233: log likelihood =  620.27797  (backed up)
Iteration 234: log likelihood =  620.27797  (backed up)
Iteration 235: log likelihood =  620.27808  (backed up)
Iteration 236: log likelihood =  620.27819  (backed up)
Iteration 237: log likelihood =  620.27832  (backed up)
Iteration 238: log likelihood =  620.27836  
Iteration 239: log likelihood =  620.27858  
(switching optimization to BHHH)
Iteration 240: log likelihood =  620.27872  
Iteration 241: log likelihood =  620.27913  (backed up)
Iteration 242: log likelihood =  620.27942  (backed up)
Iteration 243: log likelihood =  620.27962  (backed up)
Iteration 244: log likelihood =  620.27974  (backed up)
(switching optimization to BFGS)
Iteration 245: log likelihood =   620.2798  (backed up)
Iteration 246: log likelihood =  620.27989  
Iteration 247: log likelihood =  620.28145  
Iteration 248: log likelihood =  620.28145  
Iteration 249: log likelihood =  620.28188  
Iteration 250: log likelihood =  620.28278  
Iteration 251: log likelihood =  620.28289  
BFGS stepping has contracted, resetting BFGS Hessian (13)
Iteration 252: log likelihood =  620.28329  
Iteration 253: log likelihood =   620.2833  (backed up)
Iteration 254: log likelihood =  620.28351  (backed up)
(switching optimization to BHHH)
Iteration 255: log likelihood =  620.28359  (backed up)
Iteration 256: log likelihood =  620.28451  
Iteration 257: log likelihood =  620.28474  (backed up)
Iteration 258: log likelihood =  620.28491  (backed up)
Iteration 259: log likelihood =  620.28502  (backed up)
(switching optimization to BFGS)
Iteration 260: log likelihood =  620.28509  (backed up)
Iteration 261: log likelihood =  620.28688  
Iteration 262: log likelihood =  620.28721  (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (14)
Iteration 263: log likelihood =  620.28755  
Iteration 264: log likelihood =  620.28755  (backed up)
Iteration 265: log likelihood =  620.28756  (backed up)
Iteration 266: log likelihood =  620.28757  (backed up)
Iteration 267: log likelihood =  620.28757  (backed up)
Iteration 268: log likelihood =  620.28757  (backed up)
Iteration 269: log likelihood =  620.28763  (backed up)
(switching optimization to BHHH)
Iteration 270: log likelihood =  620.28763  
Iteration 271: log likelihood =  620.28785  (backed up)
Iteration 272: log likelihood =  620.28801  (backed up)
Iteration 273: log likelihood =  620.28811  (backed up)
Iteration 274: log likelihood =  620.28818  (backed up)
(switching optimization to BFGS)
Iteration 275: log likelihood =  620.28821  (backed up)
Iteration 276: log likelihood =  620.28852  
Iteration 277: log likelihood =  620.28914  
Iteration 278: log likelihood =  620.28932  
Iteration 279: log likelihood =  620.28953  
BFGS stepping has contracted, resetting BFGS Hessian (15)
Iteration 280: log likelihood =  620.28972  
Iteration 281: log likelihood =  620.28973  (backed up)
Iteration 282: log likelihood =  620.28974  (backed up)
Iteration 283: log likelihood =   620.2898  (backed up)
Iteration 284: log likelihood =   620.2899  (backed up)
(switching optimization to BHHH)
Iteration 285: log likelihood =  620.28997  (backed up)
Iteration 286: log likelihood =  620.29005  (backed up)
Iteration 287: log likelihood =   620.2901  (backed up)
Iteration 288: log likelihood =  620.29013  (backed up)
Iteration 289: log likelihood =  620.29015  (backed up)
(switching optimization to BFGS)
Iteration 290: log likelihood =  620.29016  (backed up)
Iteration 291: log likelihood =  620.29169  
Iteration 292: log likelihood =  620.29175  
BFGS stepping has contracted, resetting BFGS Hessian (16)
Iteration 293: log likelihood =  620.29189  
Iteration 294: log likelihood =  620.29189  (backed up)
Iteration 295: log likelihood =  620.29189  (backed up)
Iteration 296: log likelihood =  620.29189  (backed up)
Iteration 297: log likelihood =  620.29189  (backed up)
Iteration 298: log likelihood =  620.29189  (backed up)
Iteration 299: log likelihood =  620.29192  (backed up)
(switching optimization to BHHH)
Iteration 300: log likelihood =  620.29192  
Iteration 301: log likelihood =  620.29197  
Iteration 302: log likelihood =  620.29203  (backed up)
Iteration 303: log likelihood =  620.29208  (backed up)
Iteration 304: log likelihood =  620.29212  (backed up)
(switching optimization to BFGS)
Iteration 305: log likelihood =  620.29214  (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (17)
Iteration 306: log likelihood =  620.29314  
Iteration 307: log likelihood =  620.29318  (backed up)
Iteration 308: log likelihood =   620.2932  (backed up)
Iteration 309: log likelihood =   620.2932  (backed up)
Iteration 310: log likelihood =  620.29338  (backed up)
Iteration 311: log likelihood =  620.29347  (backed up)
Iteration 312: log likelihood =  620.29352  
Iteration 313: log likelihood =  620.29353  
BFGS stepping has contracted, resetting BFGS Hessian (18)
Iteration 314: log likelihood =  620.29355  
(switching optimization to BHHH)
Iteration 315: log likelihood =  620.29355  (backed up)
Iteration 316: log likelihood =  620.29355  (backed up)
Iteration 317: log likelihood =  620.29355  (backed up)
Iteration 318: log likelihood =  620.29355  (backed up)
Iteration 319: log likelihood =  620.29355  (backed up)
(switching optimization to BFGS)
BFGS stepping has contracted, resetting BFGS Hessian (19)
Iteration 320: log likelihood =  620.29355  (backed up)
Iteration 321: log likelihood =  620.29355  (backed up)
Iteration 322: log likelihood =  620.29356  (backed up)
Iteration 323: log likelihood =  620.29356  (backed up)
Iteration 324: log likelihood =  620.29357  (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (20)
Iteration 325: log likelihood =   620.2936  (backed up)
Iteration 326: log likelihood =   620.2936  (backed up)
Iteration 327: log likelihood =   620.2936  (backed up)
Iteration 328: log likelihood =   620.2936  (backed up)
Iteration 329: log likelihood =   620.2936  (backed up)
(switching optimization to BHHH)
Iteration 330: log likelihood =  620.29362  (backed up)
Iteration 331: log likelihood =  620.29372  
Iteration 332: log likelihood =   620.2938  (backed up)
Iteration 333: log likelihood =  620.29386  (backed up)
Iteration 334: log likelihood =   620.2939  (backed up)
(switching optimization to BFGS)
Iteration 335: log likelihood =  620.29394  (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (21)
Iteration 336: log likelihood =  620.29469  
Iteration 337: log likelihood =   620.2947  (backed up)
Iteration 338: log likelihood =  620.29471  (backed up)
Iteration 339: log likelihood =  620.29471  (backed up)
Iteration 340: log likelihood =   620.2948  (backed up)
Iteration 341: log likelihood =  620.29485  (backed up)
Iteration 342: log likelihood =  620.29487  
BFGS stepping has contracted, resetting BFGS Hessian (22)
Iteration 343: log likelihood =  620.29488  
Iteration 344: log likelihood =  620.29493  (backed up)
(switching optimization to BHHH)
Iteration 345: log likelihood =  620.29493  (backed up)
Iteration 346: log likelihood =  620.29514  
Iteration 347: log likelihood =  620.29516  
Iteration 348: log likelihood =  620.29521  (backed up)
Iteration 349: log likelihood =  620.29526  (backed up)
(switching optimization to BFGS)
Iteration 350: log likelihood =   620.2953  (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (23)
Iteration 351: log likelihood =  620.29591  
Iteration 352: log likelihood =  620.29592  (backed up)
Iteration 353: log likelihood =  620.29592  (backed up)
Iteration 354: log likelihood =  620.29592  (backed up)
Iteration 355: log likelihood =  620.29598  (backed up)
Iteration 356: log likelihood =  620.29599  (backed up)
Iteration 357: log likelihood =  620.29603  
BFGS stepping has contracted, resetting BFGS Hessian (24)
Iteration 358: log likelihood =  620.29614  
Iteration 359: log likelihood =  620.29615  (backed up)
(switching optimization to BHHH)
Iteration 360: log likelihood =  620.29615  (backed up)
Iteration 361: log likelihood =  620.29633  
Iteration 362: log likelihood =   620.2964  
Iteration 363: log likelihood =   620.2964  
Iteration 364: log likelihood =  620.29644  (backed up)
(switching optimization to BFGS)
Iteration 365: log likelihood =  620.29648  (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (25)
Iteration 366: log likelihood =  620.29697  
Iteration 367: log likelihood =  620.29697  (backed up)
Iteration 368: log likelihood =  620.29697  (backed up)
Iteration 369: log likelihood =  620.29698  (backed up)
Iteration 370: log likelihood =   620.2971  (backed up)
Iteration 371: log likelihood =  620.29716  (backed up)
Iteration 372: log likelihood =  620.29716  
BFGS stepping has contracted, resetting BFGS Hessian (26)
Iteration 373: log likelihood =  620.29717  
Iteration 374: log likelihood =   620.2972  (backed up)
(switching optimization to BHHH)
Iteration 375: log likelihood =   620.2972  (backed up)
Iteration 376: log likelihood =  620.29733  
Iteration 377: log likelihood =  620.29741  
Iteration 378: log likelihood =  620.29745  
Iteration 379: log likelihood =  620.29746  
(switching optimization to BFGS)
Iteration 380: log likelihood =  620.29749  (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (27)
Iteration 381: log likelihood =  620.29785  
Iteration 382: log likelihood =  620.29785  (backed up)
Iteration 383: log likelihood =  620.29786  (backed up)
Iteration 384: log likelihood =  620.29786  (backed up)
Iteration 385: log likelihood =  620.29794  (backed up)
Iteration 386: log likelihood =  620.29797  (backed up)
Iteration 387: log likelihood =  620.29799  
Iteration 388: log likelihood =  620.29799  
BFGS stepping has contracted, resetting BFGS Hessian (28)
Iteration 389: log likelihood =  620.29803  
(switching optimization to BHHH)
Iteration 390: log likelihood =  620.29803  (backed up)
Iteration 391: log likelihood =  620.29804  
Iteration 392: log likelihood =  620.29806  
Iteration 393: log likelihood =  620.29806  
Iteration 394: log likelihood =  620.29806  
(switching optimization to BFGS)
Iteration 395: log likelihood =  620.29809  (backed up)
Iteration 396: log likelihood =  620.29847  
Iteration 397: log likelihood =  620.29856  
BFGS stepping has contracted, resetting BFGS Hessian (29)
Iteration 398: log likelihood =  620.29857  
Iteration 399: log likelihood =  620.29859  (backed up)
Iteration 400: log likelihood =  620.29859  (backed up)
Iteration 401: log likelihood =  620.29859  (backed up)
Iteration 402: log likelihood =  620.29864  (backed up)
Iteration 403: log likelihood =  620.29869  (backed up)
Iteration 404: log likelihood =  620.29869  
(switching optimization to BHHH)
Iteration 405: log likelihood =  620.29871  
Iteration 406: log likelihood =  620.29875  
Iteration 407: log likelihood =  620.29879  
Iteration 408: log likelihood =  620.29881  
Iteration 409: log likelihood =  620.29883  
(switching optimization to BFGS)
Iteration 410: log likelihood =  620.29885  
Iteration 411: log likelihood =  620.29899  
Iteration 412: log likelihood =  620.29903  
BFGS stepping has contracted, resetting BFGS Hessian (30)
Iteration 413: log likelihood =  620.29909  
Iteration 414: log likelihood =  620.29909  (backed up)
Iteration 415: log likelihood =   620.2991  (backed up)
Iteration 416: log likelihood =  620.29911  (backed up)
Iteration 417: log likelihood =  620.29915  (backed up)
Iteration 418: log likelihood =  620.29918  (backed up)
Iteration 419: log likelihood =  620.29919  
(switching optimization to BHHH)
Iteration 420: log likelihood =  620.29919  
Iteration 421: log likelihood =   620.2992  
Iteration 422: log likelihood =  620.29921  
Iteration 423: log likelihood =  620.29923  
Iteration 424: log likelihood =  620.29924  
(switching optimization to BFGS)
Iteration 425: log likelihood =  620.29926  
BFGS stepping has contracted, resetting BFGS Hessian (31)
Iteration 426: log likelihood =  620.29945  
Iteration 427: log likelihood =  620.29945  (backed up)
Iteration 428: log likelihood =  620.29946  (backed up)
Iteration 429: log likelihood =  620.29946  (backed up)
Iteration 430: log likelihood =  620.29948  (backed up)
Iteration 431: log likelihood =  620.29949  (backed up)
Iteration 432: log likelihood =   620.2995  
Iteration 433: log likelihood =   620.2995  
BFGS stepping has contracted, resetting BFGS Hessian (32)
Iteration 434: log likelihood =  620.29952  
(switching optimization to BHHH)
Iteration 435: log likelihood =  620.29952  (backed up)
Iteration 436: log likelihood =  620.29957  
Iteration 437: log likelihood =  620.29958  
Iteration 438: log likelihood =  620.29959  
Iteration 439: log likelihood =  620.29959  
(switching optimization to BFGS)
Iteration 440: log likelihood =  620.29959  
BFGS stepping has contracted, resetting BFGS Hessian (33)
Iteration 441: log likelihood =  620.29973  
Iteration 442: log likelihood =  620.29973  (backed up)
Iteration 443: log likelihood =  620.29974  (backed up)
Iteration 444: log likelihood =  620.29974  (backed up)
Iteration 445: log likelihood =  620.29975  (backed up)
Iteration 446: log likelihood =  620.29976  (backed up)
Iteration 447: log likelihood =  620.29976  (backed up)
Iteration 448: log likelihood =  620.29976  
BFGS stepping has contracted, resetting BFGS Hessian (34)
Iteration 449: log likelihood =  620.29977  
(switching optimization to BHHH)
Iteration 450: log likelihood =  620.29977  (backed up)
Iteration 451: log likelihood =   620.2998  
Iteration 452: log likelihood =  620.29981  
Iteration 453: log likelihood =  620.29981  
Iteration 454: log likelihood =  620.29982  
(switching optimization to BFGS)
Iteration 455: log likelihood =  620.29983  
BFGS stepping has contracted, resetting BFGS Hessian (35)
Iteration 456: log likelihood =  620.29987  
Iteration 457: log likelihood =  620.29987  (backed up)
Iteration 458: log likelihood =  620.29987  (backed up)
Iteration 459: log likelihood =  620.29987  (backed up)
Iteration 460: log likelihood =  620.29988  (backed up)
Iteration 461: log likelihood =  620.29988  (backed up)
Iteration 462: log likelihood =  620.29988  
BFGS stepping has contracted, resetting BFGS Hessian (36)
Iteration 463: log likelihood =   620.2999  
Iteration 464: log likelihood =   620.2999  (backed up)
(switching optimization to BHHH)
Iteration 465: log likelihood =   620.2999  (backed up)
Iteration 466: log likelihood =  620.29992  
Iteration 467: log likelihood =  620.29993  
Iteration 468: log likelihood =  620.29993  
Iteration 469: log likelihood =  620.29994  
(switching optimization to BFGS)
Iteration 470: log likelihood =  620.29994  
BFGS stepping has contracted, resetting BFGS Hessian (37)
Iteration 471: log likelihood =  620.29998  
Iteration 472: log likelihood =  620.29998  (backed up)
Iteration 473: log likelihood =  620.29998  (backed up)
Iteration 474: log likelihood =  620.29998  (backed up)
Iteration 475: log likelihood =  620.29999  (backed up)
Iteration 476: log likelihood =      620.3  (backed up)
Iteration 477: log likelihood =      620.3  
Iteration 478: log likelihood =      620.3  
Iteration 479: log likelihood =      620.3  
(switching optimization to BHHH)
Iteration 480: log likelihood =  620.30001  
Iteration 481: log likelihood =  620.30001  
Iteration 482: log likelihood =  620.30001  
Iteration 483: log likelihood =  620.30002  
Iteration 484: log likelihood =  620.30002  
(switching optimization to BFGS)
Iteration 485: log likelihood =  620.30002  
BFGS stepping has contracted, resetting BFGS Hessian (38)
Iteration 486: log likelihood =  620.30003  
Iteration 487: log likelihood =  620.30003  (backed up)
Iteration 488: log likelihood =  620.30003  (backed up)
Iteration 489: log likelihood =  620.30003  (backed up)
Iteration 490: log likelihood =  620.30003  (backed up)
Iteration 491: log likelihood =  620.30004  
Iteration 492: log likelihood =  620.30004  
BFGS stepping has contracted, resetting BFGS Hessian (39)
Iteration 493: log likelihood =  620.30004  
Iteration 494: log likelihood =  620.30004  (backed up)
(switching optimization to BHHH)
Iteration 495: log likelihood =  620.30004  (backed up)
Iteration 496: log likelihood =  620.30004  
Iteration 497: log likelihood =  620.30004  
Iteration 498: log likelihood =  620.30004  
Iteration 499: log likelihood =  620.30004  
(switching optimization to BFGS)
BFGS stepping has contracted, resetting BFGS Hessian (40)
Iteration 500: log likelihood =  620.30004  
Iteration 501: log likelihood =  620.30004  (backed up)
Iteration 502: log likelihood =  620.30004  (backed up)
Iteration 503: log likelihood =  620.30004  (backed up)
Iteration 504: log likelihood =  620.30004  (backed up)
Iteration 505: log likelihood =  620.30004  
BFGS stepping has contracted, resetting BFGS Hessian (41)
Iteration 506: log likelihood =  620.30004  
Iteration 507: log likelihood =  620.30004  (backed up)
Iteration 508: log likelihood =  620.30004  (backed up)
Iteration 509: log likelihood =  620.30004  (backed up)
(switching optimization to BHHH)
Iteration 510: log likelihood =  620.30004  (backed up)
Iteration 511: log likelihood =  620.30004  (backed up)
Iteration 512: log likelihood =  620.30004  (backed up)
Iteration 513: log likelihood =  620.30004  (backed up)
Iteration 514: log likelihood =  620.30004  (backed up)
(switching optimization to BFGS)
Iteration 515: log likelihood =  620.30004  (backed up)
Iteration 516: log likelihood =  620.30004  (backed up)
Iteration 517: log likelihood =  620.30004  (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (42)
Iteration 518: log likelihood =  620.30004  
Iteration 519: log likelihood =  620.30004  (backed up)
Iteration 520: log likelihood =  620.30004  (backed up)
Iteration 521: log likelihood =  620.30004  (backed up)
Iteration 522: log likelihood =  620.30004  (backed up)
Iteration 523: log likelihood =  620.30004  (backed up)
Iteration 524: log likelihood =  620.30004  (backed up)
(switching optimization to BHHH)
Iteration 525: log likelihood =  620.30004  (backed up)
Iteration 526: log likelihood =  620.30004  (backed up)
Iteration 527: log likelihood =  620.30004  (backed up)
Iteration 528: log likelihood =  620.30004  (backed up)
Iteration 529: log likelihood =  620.30004  (backed up)
(switching optimization to BFGS)
BFGS stepping has contracted, resetting BFGS Hessian (43)
Iteration 530: log likelihood =  620.30004  (backed up)
Iteration 531: log likelihood =  620.30004  (backed up)
Iteration 532: log likelihood =  620.30004  (backed up)
Iteration 533: log likelihood =  620.30004  (backed up)
Iteration 534: log likelihood =  620.30004  (backed up)
Iteration 535: log likelihood =  620.30004  (backed up)
Iteration 536: log likelihood =  620.30004  (backed up)
Iteration 537: log likelihood =  620.30004  (backed up)
Iteration 538: log likelihood =  620.30004  (backed up)
Iteration 539: log likelihood =  620.30004  (backed up)
(switching optimization to BHHH)
Iteration 540: log likelihood =  620.30004  (backed up)
Iteration 541: log likelihood =  620.30004  (backed up)
Iteration 542: log likelihood =  620.30004  (backed up)
Iteration 543: log likelihood =  620.30004  (backed up)
Iteration 544: log likelihood =  620.30004  (backed up)
(switching optimization to BFGS)
Iteration 545: log likelihood =  620.30004  (backed up)
Iteration 546: log likelihood =  620.30004  (backed up)
Iteration 547: log likelihood =  620.30004  (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (44)
Iteration 548: log likelihood =  620.30004  (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (45)
Iteration 549: log likelihood =  620.30004  (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (46)
Iteration 550: log likelihood =  620.30004  (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (47)
Iteration 551: log likelihood =  620.30004  (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (48)
Iteration 552: log likelihood =  620.30004  (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (49)
Iteration 553: log likelihood =  620.30004  (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (50)
Iteration 554: log likelihood =  620.30004  (backed up)
(switching optimization to BHHH)
Iteration 555: log likelihood =  620.30004  

ARCH family regression -- multiplicative heteroskedasticity

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =     620.3                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
dell_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dell_returns |
       _cons |   .0001058   .0013711     0.08   0.938    -.0025815    .0027931
-------------+----------------------------------------------------------------
HET          |
     bmonday |   1.543887   .2293301     6.73   0.000     1.094408    1.993365
        debt |  -.5901816   .2080568    -2.84   0.005    -.9979655   -.1823977
   csanction |   .9449783   .1558664     6.06   0.000     .6394857    1.250471
       _cons |  -10.37938   .3355425   -30.93   0.000    -11.03703   -9.721728
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |  -.1278439   .0265714    -4.81   0.000    -.1799229   -.0757649
             |
       garch |
         L1. |   1.031243   .0092119   111.95   0.000     1.013188    1.049298
------------------------------------------------------------------------------
(est7 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    44.1840
 Prob > chi2(40)           =     0.2993

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    36.1427
 Prob > chi2(40)           =     0.6446

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant csanction Sanction debt DebtDe
> bate bmonday BlackMonday) nomtitles title(Dell 2011) nodep

Dell 2011
-----------------------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)          (7)   
-----------------------------------------------------------------------------------------------------
dell_ret~s                                                                                           
Constant       0.000        0.000        0.000        0.000        0.000        0.001        0.000   
             (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.002)      (0.001)   
-----------------------------------------------------------------------------------------------------
ARMA                                                                                                 
L.ar          -0.071        0.870***     0.871***    -0.072                                          
             (0.062)      (0.053)      (0.053)      (0.067)                                          
L.ma                       -0.956***    -0.956***                                                    
                          (0.037)      (0.037)                                                       
-----------------------------------------------------------------------------------------------------
SIGMA2                                                                                               
Constant       0.001***     0.001***                                                                 
             (0.000)      (0.000)                                                                    
-----------------------------------------------------------------------------------------------------
ARCH                                                                                                 
L.arch                                   0.001        0.014        0.018        0.078       -0.128***
                                       (0.031)      (0.044)      (0.045)      (0.051)      (0.027)   
L.garch                                                                         0.780***     1.031***
                                                                              (0.154)      (0.009)   
Constant                                 0.001***     0.001***     0.001***     0.000                
                                       (0.000)      (0.000)      (0.000)      (0.000)                
-----------------------------------------------------------------------------------------------------
HET                                                                                                  
BlackMon~y                                                                                   1.544***
                                                                                           (0.229)   
DebtDebate                                                                                  -0.590***
                                                                                           (0.208)   
Sanction                                                                                     0.945***
                                                                                           (0.156)   
Constant                                                                                   -10.379***
                                                                                           (0.336)   
-----------------------------------------------------------------------------------------------------
N                252          252          252          252          252          252          252   
aic        -1165.229    -1169.351    -1167.353    -1163.348    -1164.127    -1174.668    -1226.600   
bic        -1154.641    -1155.233    -1149.706    -1149.230    -1153.539    -1160.550    -1201.894   
-----------------------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/dell2011.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant c
> sanction Sanction debt DebtDebate bmonday BlackMonday) nomtitles title(Dell 2011) nodep replace
(note: file rawtables/dell2011.tex not found)
(output written to rawtables/dell2011.tex)

.           
.   * Dow (1989)
.   
.     * Clear
.           clear

.           
.         * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen dd_returns = ln(dd_close/dd_close[_n-1])
(3,148 missing values generated)

.     
.         * Sanctions
.           gen tsanction3 = 1 if date > td("04jun1989") & date < td("25may1990")
(12,847 missing values generated)

.       recode tsanction3(.=0)
(tsanction3: 12847 changes made)

. 
.         * Bond Market Crash    
.           gen f13minicrash = 1 if date > td("11oct1989") & date < td("17oct1989")
(13,091 missing values generated)

.           recode f13minicrash(.=0)
(f13minicrash: 13091 changes made)

.           
.         * Limit Time Period
.       keep if date > td("30dec1988") & date < td("01jun1990")
(12,737 observations deleted)

.                 
.     * Set for analysis
.           tsset t
        time variable:  t, 5278 to 5634
                delta:  1 unit

.           
.         * Table A.9 Models  
.       eststo clear      

.           
.           eststo: arch dd_returns, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1054.3633  
Iteration 1:   log likelihood =  1054.8134  
Iteration 2:   log likelihood =  1054.8683  
Iteration 3:   log likelihood =  1054.8724  
Iteration 4:   log likelihood =  1054.8732  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1054.8734  
Iteration 6:   log likelihood =  1054.8735  

Time-series regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       3.85
Log likelihood =  1054.873                        Prob > chi2     =     0.0497

------------------------------------------------------------------------------
             |                 OPG
  dd_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dd_returns   |
       _cons |   .0001738   .0007411     0.23   0.815    -.0012787    .0016262
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0733816   .0373933     1.96   0.050     .0000922    .1466711
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0001588   7.23e-06    21.96   0.000     .0001447     .000173
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    51.3505
 Prob > chi2(40)           =     0.1077

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    39.0605
 Prob > chi2(40)           =     0.5124

.           
.           eststo: arch dd_returns, ar(1) ma(1) 

(setting optimization to BHHH)
Iteration 0:   log likelihood =   1054.369  
Iteration 1:   log likelihood =   1054.716  
Iteration 2:   log likelihood =  1054.8225  
Iteration 3:   log likelihood =  1054.8655  
Iteration 4:   log likelihood =  1054.8788  
(switching optimization to BFGS)
BFGS stepping has contracted, resetting BFGS Hessian (0)
Iteration 5:   log likelihood =  1054.8833  
Iteration 6:   log likelihood =  1054.8845  (backed up)
Iteration 7:   log likelihood =  1054.8845  (backed up)
Iteration 8:   log likelihood =  1054.8845  (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (1)
Iteration 9:   log likelihood =  1054.8859  
Iteration 10:  log likelihood =   1054.886  (backed up)
Iteration 11:  log likelihood =   1054.886  (backed up)
Iteration 12:  log likelihood =   1054.886  (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (2)
Iteration 13:  log likelihood =  1054.8861  
Iteration 14:  log likelihood =  1054.8861  (backed up)
(switching optimization to BHHH)
Iteration 15:  log likelihood =  1054.8861  (backed up)
Iteration 16:  log likelihood =  1054.8861  

Time-series regression -- ARMA disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(2)    =       3.77
Log likelihood =  1054.886                        Prob > chi2     =     0.1520

------------------------------------------------------------------------------
             |                 OPG
  dd_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dd_returns   |
       _cons |   .0001747   .0007372     0.24   0.813    -.0012701    .0016196
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.0347623   .7614228    -0.05   0.964    -1.527124    1.457599
             |
          ma |
         L1. |   .1086962   .7655618     0.14   0.887    -1.391777     1.60917
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0001588   7.44e-06    21.34   0.000     .0001442    .0001734
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2

. 
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    51.6979
 Prob > chi2(40)           =     0.1018

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    38.8416
 Prob > chi2(40)           =     0.5223

.           
.           eststo: arch dd_returns, ar(1) arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1057.6605  
Iteration 1:   log likelihood =  1058.0653  
Iteration 2:   log likelihood =  1058.7204  
Iteration 3:   log likelihood =  1058.8617  
Iteration 4:   log likelihood =  1058.9827  
(switching optimization to BFGS)
Iteration 5:   log likelihood =   1059.089  
Iteration 6:   log likelihood =  1059.1691  
Iteration 7:   log likelihood =  1059.1748  
Iteration 8:   log likelihood =  1059.1748  

ARCH family regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       2.82
Log likelihood =  1059.175                        Prob > chi2     =     0.0929

------------------------------------------------------------------------------
             |                 OPG
  dd_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dd_returns   |
       _cons |   .0000898   .0007787     0.12   0.908    -.0014364     .001616
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .1045834    .062241     1.68   0.093    -.0174069    .2265736
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1058128   .0831274     1.27   0.203    -.0571139    .2687396
             |
       _cons |   .0001408   8.80e-06    15.99   0.000     .0001235     .000158
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    51.9614
 Prob > chi2(40)           =     0.0974

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    24.6978
 Prob > chi2(40)           =     0.9725

.           
.           eststo: arch dd_returns, ar(1) arch(1) garch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1057.2549  
Iteration 1:   log likelihood =  1058.9982  
Iteration 2:   log likelihood =   1060.145  
Iteration 3:   log likelihood =  1060.4594  
Iteration 4:   log likelihood =  1060.5916  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1060.6805  
Iteration 6:   log likelihood =  1060.7565  
Iteration 7:   log likelihood =  1060.7652  
Iteration 8:   log likelihood =  1060.7668  
Iteration 9:   log likelihood =  1060.7668  

ARCH family regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       3.38
Log likelihood =  1060.767                        Prob > chi2     =     0.0659

------------------------------------------------------------------------------
             |                 OPG
  dd_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dd_returns   |
       _cons |   -.000129   .0007514    -0.17   0.864    -.0016018    .0013437
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .1108433   .0602761     1.84   0.066    -.0072958    .2289824
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1540209   .0980675     1.57   0.116    -.0381879    .3462297
             |
       garch |
         L1. |  -.3522556   .1112646    -3.17   0.002    -.5703302   -.1341811
             |
       _cons |   .0001896   .0000203     9.36   0.000     .0001499    .0002293
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    52.7011
 Prob > chi2(40)           =     0.0861

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    24.0491
 Prob > chi2(40)           =     0.9783

.           
.           eststo: arch dd_returns, ar(1) arch(1,3)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1059.0384  
Iteration 1:   log likelihood =  1061.1237  
Iteration 2:   log likelihood =  1063.7218  
Iteration 3:   log likelihood =  1064.3818  
Iteration 4:   log likelihood =  1064.9961  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1065.3225  
Iteration 6:   log likelihood =  1066.2079  
Iteration 7:   log likelihood =  1066.4051  
Iteration 8:   log likelihood =  1066.4236  
Iteration 9:   log likelihood =  1066.4241  
Iteration 10:  log likelihood =  1066.4241  

ARCH family regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       7.86
Log likelihood =  1066.424                        Prob > chi2     =     0.0051

------------------------------------------------------------------------------
             |                 OPG
  dd_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dd_returns   |
       _cons |  -.0000927   .0006849    -0.14   0.892    -.0014352    .0012497
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .1507946   .0537867     2.80   0.005     .0453745    .2562147
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1145461   .0673774     1.70   0.089    -.0175112    .2466034
         L3. |   .3064077   .0719678     4.26   0.000     .1653535     .447462
             |
       _cons |    .000099   .0000106     9.36   0.000     .0000782    .0001197
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    50.4814
 Prob > chi2(40)           =     0.1239

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    22.5619
 Prob > chi2(40)           =     0.9881

.           
.           eststo: arch dd_returns, ar(1) arch(1,3) het(tsanction3 f13minicrash)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1080.2603  
Iteration 1:   log likelihood =  1081.0781  
Iteration 2:   log likelihood =  1081.2089  
Iteration 3:   log likelihood =   1081.212  
Iteration 4:   log likelihood =  1081.2126  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1081.2127  
Iteration 6:   log likelihood =  1081.2128  
Iteration 7:   log likelihood =  1081.2128  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       4.03
Log likelihood =  1081.213                        Prob > chi2     =     0.0446

------------------------------------------------------------------------------
             |                 OPG
  dd_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dd_returns   |
       _cons |   .0003036   .0006763     0.45   0.653    -.0010218    .0016291
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .1114397   .0554892     2.01   0.045     .0026829    .2201965
-------------+----------------------------------------------------------------
HET          |
  tsanction3 |   .3630797   .1798911     2.02   0.044     .0104997    .7156597
f13minicrash |   3.013884   1.197919     2.52   0.012     .6660063    5.361762
       _cons |   -9.38945   .1688518   -55.61   0.000    -9.720393   -9.058506
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |    .090131   .0663468     1.36   0.174    -.0399063    .2201684
         L3. |   .1169755   .0721344     1.62   0.105    -.0244053    .2583562
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    45.9414
 Prob > chi2(40)           =     0.2396

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    27.7046
 Prob > chi2(40)           =     0.9294

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant tsanction3 Sanction f13minicra
> sh MiniCrash) nomtitles title(Dow 1989) nodep

Dow 1989
----------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)   
----------------------------------------------------------------------------------------
dd_returns                                                                              
Constant       0.000        0.000        0.000       -0.000       -0.000        0.000   
             (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)   
----------------------------------------------------------------------------------------
ARMA                                                                                    
L.ar           0.073**     -0.035        0.105*       0.111*       0.151***     0.111** 
             (0.037)      (0.761)      (0.062)      (0.060)      (0.054)      (0.055)   
L.ma                        0.109                                                       
                          (0.766)                                                       
----------------------------------------------------------------------------------------
SIGMA2                                                                                  
Constant       0.000***     0.000***                                                    
             (0.000)      (0.000)                                                       
----------------------------------------------------------------------------------------
ARCH                                                                                    
L.arch                                   0.106        0.154        0.115*       0.090   
                                       (0.083)      (0.098)      (0.067)      (0.066)   
L3.arch                                                            0.306***     0.117   
                                                                 (0.072)      (0.072)   
L.garch                                              -0.352***                          
                                                    (0.111)                             
Constant                                 0.000***     0.000***     0.000***             
                                       (0.000)      (0.000)      (0.000)                
----------------------------------------------------------------------------------------
HET                                                                                     
Sanction                                                                        0.363** 
                                                                              (0.180)   
MiniCrash                                                                       3.014** 
                                                                              (1.198)   
Constant                                                                       -9.389***
                                                                              (0.169)   
----------------------------------------------------------------------------------------
N                357          357          357          357          357          357   
aic        -2103.747    -2101.772    -2110.350    -2111.534    -2122.848    -2148.426   
bic        -2092.114    -2086.261    -2094.839    -2092.145    -2103.459    -2121.281   
----------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/dow1989.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant ts
> anction3 Sanction f13minicrash MiniCrash) nomtitles title(Dow 1989) nodep replace
(note: file rawtables/dow1989.tex not found)
(output written to rawtables/dow1989.tex)

.           
.   * Dow (2011)
.   
.     * Clear
.           clear

.           
.         * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen dd_returns = ln(dd_close/dd_close[_n-1])
(3,148 missing values generated)

.           
.     * Currency Sanctions
.       gen csanction = 1 if date > td("11jul2011") & date < td("18oct2011")
(13,025 missing values generated)

.       recode csanction(.=0)
(csanction: 13025 changes made)

. 
.     * Debt Debate
.       gen debt = 1 if date > td("13may2011") & date < td("01aug2011")
(13,041 missing values generated)

.       recode debt(.=0)
(debt: 13041 changes made)

. 
.     * Black Mondayd             
.       gen bmonday = 1 if date > td("01aug2011") & date < td("15aug2011")
(13,085 missing values generated)

.       recode bmonday(.=0)
(bmonday: 13085 changes made)

. 
.     * Limit Time Period
.       keep if date > td("31dec2010") & date < td("03jan2012")   
(12,842 observations deleted)

.                 
.     * Set for analysis
.           tsset t
        time variable:  t, 10825 to 11076
                delta:  1 unit

.           
.         * Table A.10 Models
.           eststo clear

.           
.           eststo: arch dd_returns, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  549.14953  
Iteration 1:   log likelihood =  550.15966  
Iteration 2:   log likelihood =  550.39934  
Iteration 3:   log likelihood =  550.41452  
Iteration 4:   log likelihood =  550.41784  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  550.41862  
Iteration 6:   log likelihood =  550.41886  
Iteration 7:   log likelihood =  550.41886  

Time-series regression -- AR disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       0.27
Log likelihood =  550.4189                        Prob > chi2     =     0.6038

------------------------------------------------------------------------------
             |                 OPG
  dd_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dd_returns   |
       _cons |   -.000683   .0017555    -0.39   0.697    -.0041237    .0027576
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.0249177   .0480153    -0.52   0.604     -.119026    .0691906
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0007419   .0000444    16.71   0.000     .0006549    .0008289
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    64.7944
 Prob > chi2(40)           =     0.0078

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =   156.1255
 Prob > chi2(40)           =     0.0000

.           
.           eststo: arch dd_returns, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  547.44161  
Iteration 1:   log likelihood =  548.16555  
Iteration 2:   log likelihood =  550.23948  
Iteration 3:   log likelihood =  550.50662  (backed up)
Iteration 4:   log likelihood =  551.01965  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  552.49431  
Iteration 6:   log likelihood =  554.48441  
Iteration 7:   log likelihood =   554.5671  
Iteration 8:   log likelihood =   555.2319  
Iteration 9:   log likelihood =  555.37953  
Iteration 10:  log likelihood =  555.40472  
Iteration 11:  log likelihood =  555.40735  
Iteration 12:  log likelihood =  555.40745  
Iteration 13:  log likelihood =  555.40745  

Time-series regression -- ARMA disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =     328.46
Log likelihood =  555.4075                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
  dd_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dd_returns   |
       _cons |  -.0006731   .0016738    -0.40   0.688    -.0039537    .0026075
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.9075037   .0638373   -14.22   0.000    -1.032623   -.7823848
             |
          ma |
         L1. |   .8220165   .0832461     9.87   0.000     .6588573    .9851758
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0007131   .0000435    16.38   0.000     .0006278    .0007985
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    58.4151
 Prob > chi2(40)           =     0.0301

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =   162.0489
 Prob > chi2(40)           =     0.0000

.           
.           eststo: arch dd_returns, ar(1,2) ma(1,2)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  551.79953  
Iteration 1:   log likelihood =  556.31238  
Iteration 2:   log likelihood =  559.58009  
Iteration 3:   log likelihood =  559.82424  
Iteration 4:   log likelihood =  559.85153  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  559.85852  
Iteration 6:   log likelihood =  559.86156  
Iteration 7:   log likelihood =  559.86167  
Iteration 8:   log likelihood =  559.86168  

Time-series regression -- ARMA disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(4)    =     461.09
Log likelihood =  559.8617                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
  dd_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dd_returns   |
       _cons |  -.0006498   .0019382    -0.34   0.737    -.0044487    .0031491
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -1.228369   .2785757    -4.41   0.000    -1.774367   -.6823703
         L2. |  -.3647707   .2552451    -1.43   0.153     -.865042    .1355006
             |
          ma |
         L1. |    1.27724   .2548906     5.01   0.000     .7776635    1.776816
         L2. |   .5206272   .2102556     2.48   0.013     .1085339    .9327206
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0006884   .0000437    15.76   0.000     .0006027     .000774
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    49.3047
 Prob > chi2(40)           =     0.1486

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =   163.1876
 Prob > chi2(40)           =     0.0000

.           
.           eststo: arch dd_returns, ar(1,2) ma(1,2) arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  555.31525  
Iteration 1:   log likelihood =  560.72124  
Iteration 2:   log likelihood =  563.19794  
Iteration 3:   log likelihood =   563.4073  
Iteration 4:   log likelihood =  563.42602  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  563.42848  
Iteration 6:   log likelihood =  563.42971  
Iteration 7:   log likelihood =  563.42985  
Iteration 8:   log likelihood =  563.42992  
Iteration 9:   log likelihood =  563.42994  
Iteration 10:  log likelihood =  563.42994  

ARCH family regression -- ARMA disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(4)    =    1080.15
Log likelihood =  563.4299                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
  dd_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dd_returns   |
       _cons |  -.0003662    .001859    -0.20   0.844    -.0040098    .0032774
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -1.402448   .2791023    -5.02   0.000    -1.949479   -.8554178
         L2. |  -.5039041   .2607834    -1.93   0.053     -1.01503     .007222
             |
          ma |
         L1. |   1.428693   .2469822     5.78   0.000     .9446165    1.912769
         L2. |   .6164754   .2058952     2.99   0.003     .2129282    1.020022
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1482506   .0592399     2.50   0.012     .0321425    .2643587
             |
       _cons |   .0005859   .0000463    12.64   0.000     .0004951    .0006767
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.7356
 Prob > chi2(40)           =     0.4379

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =   128.8261
 Prob > chi2(40)           =     0.0000

.           
.           eststo: arch dd_returns, ar(1,2) ma(1,2) arch(1) garch(1) 

(setting optimization to BHHH)
Iteration 0:   log likelihood =  558.39681  
Iteration 1:   log likelihood =  565.18651  
Iteration 2:   log likelihood =  572.89261  
Iteration 3:   log likelihood =  581.71557  
Iteration 4:   log likelihood =  586.06403  
(switching optimization to BFGS)
BFGS stepping has contracted, resetting BFGS Hessian (0)
Iteration 5:   log likelihood =  586.92952  
Iteration 6:   log likelihood =  586.93162  (backed up)
Iteration 7:   log likelihood =  586.93219  (backed up)
Iteration 8:   log likelihood =  586.96332  (backed up)
Iteration 9:   log likelihood =  586.97659  (backed up)
Iteration 10:  log likelihood =  586.99006  (backed up)
Iteration 11:  log likelihood =  586.99866  
Iteration 12:  log likelihood =  587.06921  
Iteration 13:  log likelihood =  587.15324  (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (1)
Iteration 14:  log likelihood =  587.17068  
(switching optimization to BHHH)
Iteration 15:  log likelihood =  587.17079  (backed up)
Iteration 16:  log likelihood =  587.17251  
Iteration 17:  log likelihood =   587.1726  
Iteration 18:  log likelihood =  587.17307  
Iteration 19:  log likelihood =  587.17307  

ARCH family regression -- ARMA disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(4)    =     183.23
Log likelihood =  587.1731                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
  dd_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dd_returns   |
       _cons |   .0002734   .0014729     0.19   0.853    -.0026134    .0031603
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -1.087186   .5189861    -2.09   0.036     -2.10438   -.0699924
         L2. |  -.1904517   .5080959    -0.37   0.708    -1.186301     .805398
             |
          ma |
         L1. |    1.17279   .4887067     2.40   0.016     .2149427    2.130638
         L2. |   .3253241   .4686826     0.69   0.488    -.5932769    1.243925
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .2052288   .0529855     3.87   0.000     .1013792    .3090784
             |
       garch |
         L1. |   .7797749   .0508942    15.32   0.000     .6800241    .8795256
             |
       _cons |   .0000239   .0000151     1.58   0.114    -5.73e-06    .0000535
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.9413
 Prob > chi2(40)           =     0.4290

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    42.2299
 Prob > chi2(40)           =     0.3748

.           
.           eststo: arch dd_returns, ar(1,2) ma(1,2) arch(1,2) garch(1) 

(setting optimization to BHHH)
Iteration 0:   log likelihood =  575.80835  
Iteration 1:   log likelihood =  584.03249  
Iteration 2:   log likelihood =  585.15682  
Iteration 3:   log likelihood =  585.28652  
Iteration 4:   log likelihood =  585.39364  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  586.70655  
Iteration 6:   log likelihood =  586.71902  (backed up)
Iteration 7:   log likelihood =  588.78461  
Iteration 8:   log likelihood =  588.78461  (backed up)
Iteration 9:   log likelihood =  589.10817  
Iteration 10:  log likelihood =  589.12721  
Iteration 11:  log likelihood =   591.0447  
Iteration 12:  log likelihood =  591.11802  
Iteration 13:  log likelihood =  591.36316  
Iteration 14:  log likelihood =  591.38512  
(switching optimization to BHHH)
Iteration 15:  log likelihood =  591.39499  
Iteration 16:  log likelihood =  591.39518  
Iteration 17:  log likelihood =  591.39627  
Iteration 18:  log likelihood =  591.39637  
Iteration 19:  log likelihood =  591.39638  
(switching optimization to BFGS)
Iteration 20:  log likelihood =  591.39641  
Iteration 21:  log likelihood =  591.39649  
Iteration 22:  log likelihood =  591.39653  
Iteration 23:  log likelihood =  591.39654  
Iteration 24:  log likelihood =  591.39654  

ARCH family regression -- ARMA disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(4)    =    3101.53
Log likelihood =  591.3965                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
  dd_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dd_returns   |
       _cons |   .0004359   .0014091     0.31   0.757    -.0023259    .0031977
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.9415649   .0530001   -17.77   0.000    -1.045443   -.8376866
         L2. |  -.8562989   .0516713   -16.57   0.000    -.9575728   -.7550251
             |
          ma |
         L1. |   .9881696   .0283069    34.91   0.000     .9326892     1.04365
         L2. |   .9630433   .0257888    37.34   0.000     .9124982    1.013588
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1629145   .0512794     3.18   0.001     .0624087    .2634204
         L2. |   .5835687   .1231648     4.74   0.000     .3421701    .8249672
             |
       garch |
         L1. |      .0418   .1382776     0.30   0.762    -.2292191    .3128191
             |
       _cons |   .0002073    .000062     3.34   0.001     .0000858    .0003289
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    45.2753
 Prob > chi2(40)           =     0.2612

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    65.8698
 Prob > chi2(40)           =     0.0061

.           
.           eststo: arch dd_returns, ar(1,2) ma(1,2) arch(1,2) garch(1) het(bmonday debt csanction)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  583.78605  
Iteration 1:   log likelihood =  593.54534  
Iteration 2:   log likelihood =   594.2668  
Iteration 3:   log likelihood =  594.27841  
Iteration 4:   log likelihood =  596.06598  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  596.57613  
Iteration 6:   log likelihood =  596.72203  
Iteration 7:   log likelihood =  596.74749  
Iteration 8:   log likelihood =  596.84972  
Iteration 9:   log likelihood =   596.9415  
Iteration 10:  log likelihood =   596.9977  
Iteration 11:  log likelihood =    597.048  
Iteration 12:  log likelihood =  597.05331  
Iteration 13:  log likelihood =  597.05464  
Iteration 14:  log likelihood =  597.05564  
(switching optimization to BHHH)
Iteration 15:  log likelihood =  597.05661  
Iteration 16:  log likelihood =  597.05698  
Iteration 17:  log likelihood =   597.0571  
Iteration 18:  log likelihood =  597.05717  
Iteration 19:  log likelihood =  597.05721  
(switching optimization to BFGS)
Iteration 20:  log likelihood =  597.05723  
Iteration 21:  log likelihood =  597.05728  
Iteration 22:  log likelihood =  597.05729  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(4)    =     126.70
Log likelihood =  597.0573                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
  dd_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dd_returns   |
       _cons |   .0000501   .0014009     0.04   0.971    -.0026957     .002796
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -1.009144   .8476699    -1.19   0.234    -2.670546    .6522589
         L2. |  -.0818855   .8177643    -0.10   0.920    -1.684674    1.520903
             |
          ma |
         L1. |   1.064126   .8335342     1.28   0.202    -.5695708    2.697823
         L2. |   .1628078   .7896379     0.21   0.837    -1.384854     1.71047
-------------+----------------------------------------------------------------
HET          |
     bmonday |    1.94765    .826909     2.36   0.019     .3269377    3.568361
        debt |  -.5709916   .4597425    -1.24   0.214     -1.47207    .3300871
   csanction |   1.073754   .5036395     2.13   0.033     .0866383    2.060869
       _cons |  -10.06075   .6115233   -16.45   0.000    -11.25931   -8.862186
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |    .074188   .0777804     0.95   0.340    -.0782589    .2266348
         L2. |    .059036   .0837051     0.71   0.481     -.105023     .223095
             |
       garch |
         L1. |   .7600177   .0942911     8.06   0.000     .5752106    .9448247
------------------------------------------------------------------------------
(est7 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    49.9889
 Prob > chi2(40)           =     0.1338

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    41.2416
 Prob > chi2(40)           =     0.4161

.           
.           eststo: arch dd_returns, ar(9, 25) arch(1,2) garch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  579.71973  
Iteration 1:   log likelihood =  590.66018  
Iteration 2:   log likelihood =  592.02348  
Iteration 3:   log likelihood =  592.28214  
Iteration 4:   log likelihood =  592.36233  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  592.37613  
Iteration 6:   log likelihood =  592.41784  
Iteration 7:   log likelihood =  592.42524  
Iteration 8:   log likelihood =   592.4415  
Iteration 9:   log likelihood =  592.45107  
Iteration 10:  log likelihood =  592.48015  
Iteration 11:  log likelihood =  592.48183  
Iteration 12:  log likelihood =  592.48201  
Iteration 13:  log likelihood =  592.48204  

ARCH family regression -- AR disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =      24.80
Log likelihood =   592.482                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
  dd_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dd_returns   |
       _cons |   .0002788   .0010108     0.28   0.783    -.0017023    .0022598
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L9. |  -.1522965   .0544393    -2.80   0.005    -.2589957   -.0455974
        L25. |  -.1477429   .0436992    -3.38   0.001    -.2333917   -.0620941
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |      .1801   .0533957     3.37   0.001     .0754464    .2847535
         L2. |   .5421321   .1222329     4.44   0.000     .3025601    .7817041
             |
       garch |
         L1. |   .1931663   .1732245     1.12   0.265    -.1463475    .5326801
             |
       _cons |   .0001312   .0000631     2.08   0.038     7.59e-06    .0002548
------------------------------------------------------------------------------
(est8 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    42.0172
 Prob > chi2(40)           =     0.3836

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    45.5148
 Prob > chi2(40)           =     0.2533

.           
.           eststo: arch dd_returns, ar(9, 25) arch(1,2) garch(1) het(bmonday debt csanction)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  589.69419  
Iteration 1:   log likelihood =  595.19937  
Iteration 2:   log likelihood =  600.37111  
Iteration 3:   log likelihood =  603.34907  
Iteration 4:   log likelihood =  603.49202  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  603.50931  
Iteration 6:   log likelihood =  603.51971  
Iteration 7:   log likelihood =  603.52049  
Iteration 8:   log likelihood =  603.52075  
Iteration 9:   log likelihood =   603.5208  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =      18.82
Log likelihood =  603.5208                        Prob > chi2     =     0.0001

------------------------------------------------------------------------------
             |                 OPG
  dd_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dd_returns   |
       _cons |  -.0001539   .0009736    -0.16   0.874    -.0020621    .0017543
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L9. |  -.1410133   .0630952    -2.23   0.025    -.2646777   -.0173489
        L25. |  -.1633764   .0491047    -3.33   0.001      -.25962   -.0671329
-------------+----------------------------------------------------------------
HET          |
     bmonday |   1.714608     .76926     2.23   0.026      .206886     3.22233
        debt |  -.7272067   .3961358    -1.84   0.066    -1.503619    .0492053
   csanction |   1.232119   .4217953     2.92   0.003     .4054157    2.058823
       _cons |  -9.713806   .6188779   -15.70   0.000    -10.92678   -8.500827
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0741775   .0706167     1.05   0.294    -.0642286    .2125836
         L2. |   .0609622   .0855198     0.71   0.476    -.1066535    .2285779
             |
       garch |
         L1. |   .7046916   .1264641     5.57   0.000     .4568264    .9525567
------------------------------------------------------------------------------
(est9 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.4441
 Prob > chi2(40)           =     0.5859

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    34.4765
 Prob > chi2(40)           =     0.7167

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant csanction Sanction debt DebtDe
> bate bmonday BlackMonday) nomtitles title(Dow Chemical 2011) nodep  

Dow Chemical 2011
-------------------------------------------------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)          (7)          (8)          (9)   
-------------------------------------------------------------------------------------------------------------------------------
dd_returns                                                                                                                     
Constant      -0.001       -0.001       -0.001       -0.000        0.000        0.000        0.000        0.000       -0.000   
             (0.002)      (0.002)      (0.002)      (0.002)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)   
-------------------------------------------------------------------------------------------------------------------------------
ARMA                                                                                                                           
L.ar          -0.025       -0.908***    -1.228***    -1.402***    -1.087**     -0.942***    -1.009                             
             (0.048)      (0.064)      (0.279)      (0.279)      (0.519)      (0.053)      (0.848)                             
L2.ar                                   -0.365       -0.504*      -0.190       -0.856***    -0.082                             
                                       (0.255)      (0.261)      (0.508)      (0.052)      (0.818)                             
L9.ar                                                                                                    -0.152***    -0.141** 
                                                                                                        (0.054)      (0.063)   
L25.ar                                                                                                   -0.148***    -0.163***
                                                                                                        (0.044)      (0.049)   
L.ma                        0.822***     1.277***     1.429***     1.173**      0.988***     1.064                             
                          (0.083)      (0.255)      (0.247)      (0.489)      (0.028)      (0.834)                             
L2.ma                                    0.521**      0.616***     0.325        0.963***     0.163                             
                                       (0.210)      (0.206)      (0.469)      (0.026)      (0.790)                             
-------------------------------------------------------------------------------------------------------------------------------
SIGMA2                                                                                                                         
Constant       0.001***     0.001***     0.001***                                                                              
             (0.000)      (0.000)      (0.000)                                                                                 
-------------------------------------------------------------------------------------------------------------------------------
ARCH                                                                                                                           
L.arch                                                0.148**      0.205***     0.163***     0.074        0.180***     0.074   
                                                    (0.059)      (0.053)      (0.051)      (0.078)      (0.053)      (0.071)   
L2.arch                                                                         0.584***     0.059        0.542***     0.061   
                                                                              (0.123)      (0.084)      (0.122)      (0.086)   
L.garch                                                            0.780***     0.042        0.760***     0.193        0.705***
                                                                 (0.051)      (0.138)      (0.094)      (0.173)      (0.126)   
Constant                                              0.001***     0.000        0.000***                  0.000**              
                                                    (0.000)      (0.000)      (0.000)                   (0.000)                
-------------------------------------------------------------------------------------------------------------------------------
HET                                                                                                                            
BlackMon~y                                                                                   1.948**                   1.715** 
                                                                                           (0.827)                   (0.769)   
DebtDebate                                                                                  -0.571                    -0.727*  
                                                                                           (0.460)                   (0.396)   
Sanction                                                                                     1.074**                   1.232***
                                                                                           (0.504)                   (0.422)   
Constant                                                                                   -10.061***                 -9.714***
                                                                                           (0.612)                   (0.619)   
-------------------------------------------------------------------------------------------------------------------------------
N                252          252          252          252          252          252          252          252          252   
aic        -1094.838    -1102.815    -1107.723    -1112.860    -1158.346    -1164.793    -1170.115    -1170.964    -1187.042   
bic        -1084.249    -1088.697    -1086.547    -1088.154    -1130.111    -1133.028    -1127.761    -1146.258    -1151.747   
-------------------------------------------------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/dow2011.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant cs
> anction Sanction debt DebtDebate bmonday BlackMonday) nomtitles title(Dow Chemical 2011) nodep replace 
(note: file rawtables/dow2011.tex not found)
(output written to rawtables/dow2011.tex)

.         
.   * PPG (1989)
.   
.     * Clear
.           clear

.           
.         * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen ppg_returns = ln(ppg_close/ppg_close[_n-1])
(5,138 missing values generated)

.     
.         * Sanctions
.           gen tsanction3 = 1 if date > td("04jun1989") & date < td("25may1990")
(12,847 missing values generated)

.       recode tsanction3(.=0)
(tsanction3: 12847 changes made)

. 
.         * Bond Market Crash    
.           gen f13minicrash = 1 if date > td("11oct1989") & date < td("17oct1989")
(13,091 missing values generated)

.           recode f13minicrash(.=0)
(f13minicrash: 13091 changes made)

.           
.         * Limit Time Period
.       keep if date > td("30dec1988") & date < td("01jun1990")
(12,737 observations deleted)

.                 
.     * Set for analysis
.           tsset t
        time variable:  t, 5278 to 5634
                delta:  1 unit

.           
.         * Table A.11 Models  
.       eststo clear      

.           
.           eststo: arch ppg_returns, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1092.4803  
Iteration 1:   log likelihood =  1093.7384  
Iteration 2:   log likelihood =  1093.8946  
Iteration 3:   log likelihood =  1093.9219  
Iteration 4:   log likelihood =  1093.9273  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1093.9287  
BFGS stepping has contracted, resetting BFGS Hessian (0)
Iteration 6:   log likelihood =  1093.9292  
Iteration 7:   log likelihood =  1093.9293  (backed up)
Iteration 8:   log likelihood =  1093.9293  (backed up)
Iteration 9:   log likelihood =  1093.9293  (backed up)
Iteration 10:  log likelihood =  1093.9293  

Time-series regression -- ARMA disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(2)    =       5.10
Log likelihood =  1093.929                        Prob > chi2     =     0.0782

------------------------------------------------------------------------------
             |                 OPG
 ppg_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ppg_returns  |
       _cons |    .000581   .0005466     1.06   0.288    -.0004903    .0016523
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .3088034   .5222359     0.59   0.554    -.7147601    1.332367
             |
          ma |
         L1. |  -.3897399   .5034099    -0.77   0.439    -1.376405    .5969254
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0001276   7.57e-06    16.87   0.000     .0001128    .0001425
------------------------------------------------------------------------------
(est1 stored)

.     
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    41.9119
 Prob > chi2(40)           =     0.3879

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    47.2539
 Prob > chi2(40)           =     0.2004

.           
.           eststo: arch ppg_returns, ar(11,16)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1100.3183  
Iteration 1:   log likelihood =  1100.8035  
Iteration 2:   log likelihood =  1100.8634  
Iteration 3:   log likelihood =  1100.8708  
Iteration 4:   log likelihood =  1100.8717  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1100.8718  
Iteration 6:   log likelihood =  1100.8718  

Time-series regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(2)    =      14.77
Log likelihood =  1100.872                        Prob > chi2     =     0.0006

------------------------------------------------------------------------------
             |                 OPG
 ppg_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ppg_returns  |
       _cons |   .0007256   .0008386     0.87   0.387    -.0009181    .0023693
-------------+----------------------------------------------------------------
ARMA         |
          ar |
        L11. |   .1406959   .0528207     2.66   0.008     .0371692    .2442226
        L16. |   .1597193   .0585991     2.73   0.006     .0448673    .2745714
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0001228   7.39e-06    16.61   0.000     .0001083    .0001373
------------------------------------------------------------------------------
(est2 stored)

.         
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    22.0564
 Prob > chi2(40)           =     0.9905

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    44.6542
 Prob > chi2(40)           =     0.2825

.           
.           eststo: arch ppg_returns, ar(11,16) arch(1) garch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1100.8923  
Iteration 1:   log likelihood =  1101.4068  
Iteration 2:   log likelihood =  1102.3727  
Iteration 3:   log likelihood =  1103.1684  
Iteration 4:   log likelihood =  1103.1927  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1103.1946  
BFGS stepping has contracted, resetting BFGS Hessian (0)
Iteration 6:   log likelihood =  1103.1953  
Iteration 7:   log likelihood =  1103.1954  (backed up)
Iteration 8:   log likelihood =  1103.1954  (backed up)
Iteration 9:   log likelihood =  1103.1954  (backed up)
Iteration 10:  log likelihood =  1103.1954  (backed up)
Iteration 11:  log likelihood =  1103.1954  
Iteration 12:  log likelihood =  1103.1954  (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (1)
Iteration 13:  log likelihood =  1103.1954  
Iteration 14:  log likelihood =  1103.1954  (backed up)
(switching optimization to BHHH)
Iteration 15:  log likelihood =  1103.1954  

ARCH family regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(2)    =      12.75
Log likelihood =  1103.195                        Prob > chi2     =     0.0017

------------------------------------------------------------------------------
             |                 OPG
 ppg_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ppg_returns  |
       _cons |   .0007684   .0008035     0.96   0.339    -.0008065    .0023433
-------------+----------------------------------------------------------------
ARMA         |
          ar |
        L11. |   .1358033   .0518256     2.62   0.009      .034227    .2373797
        L16. |   .1412791   .0591312     2.39   0.017     .0253841    .2571742
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0584106   .0531658     1.10   0.272    -.0457925    .1626136
             |
       garch |
         L1. |   .7365549   .2910264     2.53   0.011     .1661536    1.306956
             |
       _cons |   .0000248   .0000306     0.81   0.417    -.0000351    .0000848
------------------------------------------------------------------------------
(est3 stored)

.         
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    22.4414
 Prob > chi2(40)           =     0.9887

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.8708
 Prob > chi2(40)           =     0.5665

.           
.           eststo: arch ppg_returns, ar(11,16) arch(1,2) garch(1,2)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1097.5777  
Iteration 1:   log likelihood =  1100.3478  
Iteration 2:   log likelihood =  1102.2161  
Iteration 3:   log likelihood =  1103.7064  
Iteration 4:   log likelihood =  1104.1074  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1104.5132  
BFGS stepping has contracted, resetting BFGS Hessian (0)
Iteration 6:   log likelihood =  1104.5942  
Iteration 7:   log likelihood =  1104.5943  (backed up)
Iteration 8:   log likelihood =  1104.5943  (backed up)
Iteration 9:   log likelihood =  1104.5947  (backed up)
Iteration 10:  log likelihood =  1104.5956  (backed up)
Iteration 11:  log likelihood =  1104.5966  (backed up)
Iteration 12:  log likelihood =   1104.597  (backed up)
Iteration 13:  log likelihood =  1104.5979  
Iteration 14:  log likelihood =   1104.598  (backed up)
(switching optimization to BHHH)
Iteration 15:  log likelihood =  1104.5994  
Iteration 16:  log likelihood =  1104.5995  
Iteration 17:  log likelihood =  1104.5995  
Iteration 18:  log likelihood =  1104.5995  
Iteration 19:  log likelihood =  1104.5995  

ARCH family regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(2)    =      16.83
Log likelihood =    1104.6                        Prob > chi2     =     0.0002

------------------------------------------------------------------------------
             |                 OPG
 ppg_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ppg_returns  |
       _cons |   .0006569    .000825     0.80   0.426      -.00096    .0022737
-------------+----------------------------------------------------------------
ARMA         |
          ar |
        L11. |   .1539387   .0492133     3.13   0.002     .0574825    .2503949
        L16. |   .1538866   .0598412     2.57   0.010     .0365999    .2711733
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0139499    .055927     0.25   0.803    -.0956651    .1235648
         L2. |   .0318745   .0693982     0.46   0.646    -.1041435    .1678926
             |
       garch |
         L1. |   1.440546   .2123384     6.78   0.000     1.024371    1.856722
         L2. |  -.7414165   .1895023    -3.91   0.000    -1.112834   -.3699987
             |
       _cons |   .0000311   .0000123     2.54   0.011     7.09e-06    .0000551
------------------------------------------------------------------------------
(est4 stored)

.         
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    23.2612
 Prob > chi2(40)           =     0.9841

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    31.5069
 Prob > chi2(40)           =     0.8291

.           
.           eststo: arch ppg_returns, ar(11,16) arch(1,2) garch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1101.8271  
Iteration 1:   log likelihood =  1102.5041  
Iteration 2:   log likelihood =  1103.1301  
Iteration 3:   log likelihood =  1103.3223  
Iteration 4:   log likelihood =  1103.3409  
(switching optimization to BFGS)
BFGS stepping has contracted, resetting BFGS Hessian (0)
Iteration 5:   log likelihood =  1103.3445  
Iteration 6:   log likelihood =  1103.3448  (backed up)
Iteration 7:   log likelihood =  1103.3451  (backed up)
Iteration 8:   log likelihood =  1103.3459  (backed up)
Iteration 9:   log likelihood =   1103.346  (backed up)
Iteration 10:  log likelihood =  1103.3466  (backed up)
Iteration 11:  log likelihood =  1103.3468  
Iteration 12:  log likelihood =  1103.3471  (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (1)
Iteration 13:  log likelihood =  1103.3492  
Iteration 14:  log likelihood =  1103.3492  (backed up)
(switching optimization to BHHH)
Iteration 15:  log likelihood =  1103.3492  (backed up)
Iteration 16:  log likelihood =  1103.3492  
Iteration 17:  log likelihood =  1103.3492  
Iteration 18:  log likelihood =  1103.3492  
Iteration 19:  log likelihood =  1103.3492  

ARCH family regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(2)    =      13.76
Log likelihood =  1103.349                        Prob > chi2     =     0.0010

------------------------------------------------------------------------------
             |                 OPG
 ppg_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ppg_returns  |
       _cons |   .0007201   .0008257     0.87   0.383    -.0008983    .0023385
-------------+----------------------------------------------------------------
ARMA         |
          ar |
        L11. |   .1404354   .0519702     2.70   0.007     .0385758     .242295
        L16. |   .1539437   .0597993     2.57   0.010     .0367393    .2711481
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0257842   .0723532     0.36   0.722    -.1160256     .167594
         L2. |   .0412818    .079384     0.52   0.603     -.114308    .1968715
             |
       garch |
         L1. |   .6420207   .4107258     1.56   0.118     -.162987    1.447028
             |
       _cons |   .0000352   .0000444     0.79   0.428    -.0000518    .0001223
------------------------------------------------------------------------------
(est5 stored)

.         
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    22.5328
 Prob > chi2(40)           =     0.9882

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.3545
 Prob > chi2(40)           =     0.5900

.           
.           eststo: arch ppg_returns, ar(11,16) arch(1) garch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1100.8923  
Iteration 1:   log likelihood =  1101.4068  
Iteration 2:   log likelihood =  1102.3727  
Iteration 3:   log likelihood =  1103.1684  
Iteration 4:   log likelihood =  1103.1927  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1103.1946  
BFGS stepping has contracted, resetting BFGS Hessian (0)
Iteration 6:   log likelihood =  1103.1953  
Iteration 7:   log likelihood =  1103.1954  (backed up)
Iteration 8:   log likelihood =  1103.1954  (backed up)
Iteration 9:   log likelihood =  1103.1954  (backed up)
Iteration 10:  log likelihood =  1103.1954  (backed up)
Iteration 11:  log likelihood =  1103.1954  
Iteration 12:  log likelihood =  1103.1954  (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (1)
Iteration 13:  log likelihood =  1103.1954  
Iteration 14:  log likelihood =  1103.1954  (backed up)
(switching optimization to BHHH)
Iteration 15:  log likelihood =  1103.1954  

ARCH family regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(2)    =      12.75
Log likelihood =  1103.195                        Prob > chi2     =     0.0017

------------------------------------------------------------------------------
             |                 OPG
 ppg_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ppg_returns  |
       _cons |   .0007684   .0008035     0.96   0.339    -.0008065    .0023433
-------------+----------------------------------------------------------------
ARMA         |
          ar |
        L11. |   .1358033   .0518256     2.62   0.009      .034227    .2373797
        L16. |   .1412791   .0591312     2.39   0.017     .0253841    .2571742
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0584106   .0531658     1.10   0.272    -.0457925    .1626136
             |
       garch |
         L1. |   .7365549   .2910264     2.53   0.011     .1661536    1.306956
             |
       _cons |   .0000248   .0000306     0.81   0.417    -.0000351    .0000848
------------------------------------------------------------------------------
(est6 stored)

.         
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    22.4414
 Prob > chi2(40)           =     0.9887

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.8708
 Prob > chi2(40)           =     0.5665

.           
.           eststo: arch ppg_returns, ar(11,16) arch(1) garch(1) het(tsanction3 f13minicrash)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1104.0233  
Iteration 1:   log likelihood =  1110.2692  
Iteration 2:   log likelihood =  1113.7421  
Iteration 3:   log likelihood =  1113.8678  
Iteration 4:   log likelihood =   1115.245  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1115.4306  
Iteration 6:   log likelihood =  1115.4384  
Iteration 7:   log likelihood =  1115.4401  
Iteration 8:   log likelihood =  1115.4402  
Iteration 9:   log likelihood =  1115.4402  
Iteration 10:  log likelihood =  1115.4402  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(2)    =      12.81
Log likelihood =   1115.44                        Prob > chi2     =     0.0017

------------------------------------------------------------------------------
             |                 OPG
 ppg_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ppg_returns  |
       _cons |   .0009564   .0007323     1.31   0.192    -.0004788    .0023916
-------------+----------------------------------------------------------------
ARMA         |
          ar |
        L11. |   .1283724      .0492     2.61   0.009     .0319421    .2248027
        L16. |   .1414989   .0560699     2.52   0.012     .0316039    .2513939
-------------+----------------------------------------------------------------
HET          |
  tsanction3 |   .3601245   .1972631     1.83   0.068    -.0265041    .7467531
f13minicrash |   2.554267   .7600886     3.36   0.001      1.06452    4.044013
       _cons |   -10.4154   .6361956   -16.37   0.000    -11.66232   -9.168483
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0373774   .0518884     0.72   0.471    -.0643221    .1390768
             |
       garch |
         L1. |   .6055063   .2222472     2.72   0.006     .1699098    1.041103
------------------------------------------------------------------------------
(est7 stored)

.         
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    30.1000
 Prob > chi2(40)           =     0.8724

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    24.9268
 Prob > chi2(40)           =     0.9702

.           
.           eststo: arch ppg_returns, ar(11,16) het(tsanction3 f13minicrash)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1101.0834  
Iteration 1:   log likelihood =  1109.4031  
Iteration 2:   log likelihood =  1112.2812  
Iteration 3:   log likelihood =  1112.5733  
Iteration 4:   log likelihood =  1112.6172  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1112.6182  
Iteration 6:   log likelihood =  1112.6183  

Time-series regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(2)    =      12.83
Log likelihood =  1112.618                        Prob > chi2     =     0.0016

------------------------------------------------------------------------------
             |                 OPG
 ppg_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ppg_returns  |
       _cons |   .0010371    .000751     1.38   0.167    -.0004349     .002509
-------------+----------------------------------------------------------------
ARMA         |
          ar |
        L11. |   .1227816   .0496692     2.47   0.013     .0254317    .2201315
        L16. |   .1501202   .0566998     2.65   0.008     .0389905    .2612498
-------------+----------------------------------------------------------------
HET          |
  tsanction3 |   .3969855   .1597291     2.49   0.013     .0839223    .7100488
f13minicrash |   2.156188   .9231119     2.34   0.020      .346922    3.965454
       _cons |  -9.363818   .1276679   -73.35   0.000    -9.614042   -9.113593
------------------------------------------------------------------------------
(est8 stored)

.         
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    28.5654
 Prob > chi2(40)           =     0.9114

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    25.6490
 Prob > chi2(40)           =     0.9619

.           
.           eststo: arch ppg_returns, ar(1,11,13,16) het(tsanction3 f13minicrash)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1105.7762  
Iteration 1:   log likelihood =  1114.0927  
Iteration 2:   log likelihood =  1116.6118  
Iteration 3:   log likelihood =   1116.775  
Iteration 4:   log likelihood =  1116.7782  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1116.7785  
Iteration 6:   log likelihood =  1116.7785  

Time-series regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(4)    =      22.40
Log likelihood =  1116.779                        Prob > chi2     =     0.0002

------------------------------------------------------------------------------
             |                 OPG
 ppg_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ppg_returns  |
       _cons |   .0009517   .0005827     1.63   0.102    -.0001903    .0020937
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.1115063   .0509594    -2.19   0.029     -.211385   -.0116276
        L11. |   .1188528   .0491869     2.42   0.016     .0224482    .2152573
        L13. |  -.1007763   .0535723    -1.88   0.060    -.2057762    .0042235
        L16. |   .1444746   .0563935     2.56   0.010     .0339454    .2550038
-------------+----------------------------------------------------------------
HET          |
  tsanction3 |   .3784454   .1634527     2.32   0.021      .058084    .6988069
f13minicrash |   2.355753   .9896669     2.38   0.017     .4160413    4.295464
       _cons |  -9.375958   .1325264   -70.75   0.000    -9.635705   -9.116211
------------------------------------------------------------------------------
(est9 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    24.1243
 Prob > chi2(40)           =     0.9777

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    27.2171
 Prob > chi2(40)           =     0.9384

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant tsanction3 Sanction f13minicra
> sh MiniCrash) nomtitles title(PPG 1989) nodep

PPG 1989
-------------------------------------------------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)          (7)          (8)          (9)   
-------------------------------------------------------------------------------------------------------------------------------
ppg_retu~s                                                                                                                     
Constant       0.001        0.001        0.001        0.001        0.001        0.001        0.001        0.001        0.001   
             (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)   
-------------------------------------------------------------------------------------------------------------------------------
ARMA                                                                                                                           
L.ar           0.309                                                                                                  -0.112** 
             (0.522)                                                                                                 (0.051)   
L11.ar                      0.141***     0.136***     0.154***     0.140***     0.136***     0.128***     0.123**      0.119** 
                          (0.053)      (0.052)      (0.049)      (0.052)      (0.052)      (0.049)      (0.050)      (0.049)   
L16.ar                      0.160***     0.141**      0.154**      0.154**      0.141**      0.141**      0.150***     0.144** 
                          (0.059)      (0.059)      (0.060)      (0.060)      (0.059)      (0.056)      (0.057)      (0.056)   
L13.ar                                                                                                                -0.101*  
                                                                                                                     (0.054)   
L.ma          -0.390                                                                                                           
             (0.503)                                                                                                           
-------------------------------------------------------------------------------------------------------------------------------
SIGMA2                                                                                                                         
Constant       0.000***     0.000***                                                                                           
             (0.000)      (0.000)                                                                                              
-------------------------------------------------------------------------------------------------------------------------------
ARCH                                                                                                                           
L.arch                                   0.058        0.014        0.026        0.058        0.037                             
                                       (0.053)      (0.056)      (0.072)      (0.053)      (0.052)                             
L2.arch                                               0.032        0.041                                                       
                                                    (0.069)      (0.079)                                                       
L.garch                                  0.737**      1.441***     0.642        0.737**      0.606***                          
                                       (0.291)      (0.212)      (0.411)      (0.291)      (0.222)                             
L2.garch                                             -0.741***                                                                 
                                                    (0.190)                                                                    
Constant                                 0.000        0.000**      0.000        0.000                                          
                                       (0.000)      (0.000)      (0.000)      (0.000)                                          
-------------------------------------------------------------------------------------------------------------------------------
HET                                                                                                                            
Sanction                                                                                     0.360*       0.397**      0.378** 
                                                                                           (0.197)      (0.160)      (0.163)   
MiniCrash                                                                                    2.554***     2.156**      2.356** 
                                                                                           (0.760)      (0.923)      (0.990)   
Constant                                                                                   -10.415***    -9.364***    -9.376***
                                                                                           (0.636)      (0.128)      (0.133)   
-------------------------------------------------------------------------------------------------------------------------------
N                357          357          357          357          357          357          357          357          357   
aic        -2179.859    -2193.744    -2194.391    -2193.199    -2192.698    -2194.391    -2214.880    -2213.237    -2217.557   
bic        -2164.348    -2178.233    -2171.124    -2162.177    -2165.554    -2171.124    -2183.859    -2189.970    -2186.535   
-------------------------------------------------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/ppg1989.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant ts
> anction3 Sanction f13minicrash MiniCrash) nomtitles title(PPG 1989) nodep      replace
(note: file rawtables/ppg1989.tex not found)
(output written to rawtables/ppg1989.tex)

.           
.   * PPG (2011)
.   
.     * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen ppg_returns = ln(ppg_close/ppg_close[_n-1])
(5,138 missing values generated)

.           
.     * Currency Sanctions
.       gen csanction = 1 if date > td("11jul2011") & date < td("18oct2011")
(13,025 missing values generated)

.       recode csanction(.=0)
(csanction: 13025 changes made)

. 
.     * Debt Debate
.       gen debt = 1 if date > td("13may2011") & date < td("01aug2011")
(13,041 missing values generated)

.       recode debt(.=0)
(debt: 13041 changes made)

. 
.     * Black Mondayd             
.       gen bmonday = 1 if date > td("01aug2011") & date < td("15aug2011")
(13,085 missing values generated)

.       recode bmonday(.=0)
(bmonday: 13085 changes made)

. 
.     * Limit Time Period
.       keep if date > td("31dec2010") & date < td("03jan2012")   
(12,842 observations deleted)

.                 
.     * Set for analysis
.           tsset t
        time variable:  t, 10825 to 11076
                delta:  1 unit

.           
.         * Table A.12 Models
.           eststo clear    

.           
.           eststo: arch ppg_returns, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =   639.1643  
Iteration 1:   log likelihood =  639.75339  
Iteration 2:   log likelihood =  639.78027  
Iteration 3:   log likelihood =   639.7837  
Iteration 4:   log likelihood =  639.78416  
(switching optimization to BFGS)
Iteration 5:   log likelihood =   639.7842  

Time-series regression -- AR disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       0.78
Log likelihood =  639.7842                        Prob > chi2     =     0.3759

------------------------------------------------------------------------------
             |                 OPG
 ppg_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ppg_returns  |
       _cons |  -.0000272   .0011619    -0.02   0.981    -.0023044      .00225
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.0433497   .0489532    -0.89   0.376    -.1392962    .0525968
-------------+----------------------------------------------------------------
     /SIGMA2 |    .000365   .0000244    14.93   0.000     .0003171    .0004129
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    45.0454
 Prob > chi2(40)           =     0.2690

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =   138.8765
 Prob > chi2(40)           =     0.0000

.           
.           eststo: arch ppg_returns, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  638.69875  
Iteration 1:   log likelihood =  638.77718  
Iteration 2:   log likelihood =  640.77602  
Iteration 3:   log likelihood =  641.30217  
Iteration 4:   log likelihood =  641.69165  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  642.07413  
Iteration 6:   log likelihood =  642.25945  
Iteration 7:   log likelihood =  642.28441  
Iteration 8:   log likelihood =  642.28746  
Iteration 9:   log likelihood =  642.28757  
Iteration 10:  log likelihood =  642.28757  

Time-series regression -- ARMA disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =     161.86
Log likelihood =  642.2876                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
 ppg_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ppg_returns  |
       _cons |   -.000025   .0011604    -0.02   0.983    -.0022994    .0022494
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.8774492   .0955261    -9.19   0.000    -1.064677   -.6902215
             |
          ma |
         L1. |   .8060251   .1161459     6.94   0.000     .5783832    1.033667
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0003579    .000025    14.34   0.000     .0003089    .0004068
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    43.0823
 Prob > chi2(40)           =     0.3408

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =   137.7906
 Prob > chi2(40)           =     0.0000

.           
.           eststo: arch ppg_returns, ar(1) arch(2)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  649.56946  
Iteration 1:   log likelihood =  651.28219  
Iteration 2:   log likelihood =  651.36893  
Iteration 3:   log likelihood =   651.3903  
Iteration 4:   log likelihood =  651.39558  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  651.39681  
Iteration 6:   log likelihood =  651.39716  
Iteration 7:   log likelihood =  651.39716  

ARCH family regression -- AR disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       0.22
Log likelihood =  651.3972                        Prob > chi2     =     0.6390

------------------------------------------------------------------------------
             |                 OPG
 ppg_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ppg_returns  |
       _cons |   .0007148   .0011836     0.60   0.546    -.0016049    .0030345
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0214462   .0457207     0.47   0.639    -.0681647    .1110572
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L2. |   .2965986   .0950151     3.12   0.002     .1103724    .4828248
             |
       _cons |    .000254   .0000221    11.47   0.000     .0002106    .0002974
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    41.1693
 Prob > chi2(40)           =     0.4192

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    70.4376
 Prob > chi2(40)           =     0.0021

.           
.           eststo: arch ppg_returns, arch(2)

(setting optimization to BHHH)
Iteration 0:   log likelihood =   651.0808  
Iteration 1:   log likelihood =  651.33241  
Iteration 2:   log likelihood =  651.34277  
Iteration 3:   log likelihood =  651.34333  
Iteration 4:   log likelihood =  651.34341  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  651.34343  

ARCH family regression

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  651.3434                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 ppg_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ppg_returns  |
       _cons |   .0007081   .0011342     0.62   0.532    -.0015148     .002931
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L2. |   .2914868   .0944177     3.09   0.002     .1064316    .4765421
             |
       _cons |   .0002552   .0000222    11.51   0.000     .0002118    .0002987
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.6684
 Prob > chi2(40)           =     0.4408

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    71.8465
 Prob > chi2(40)           =     0.0015

.           
.           eststo: arch ppg_returns, arch(2,7)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  656.20984  
Iteration 1:   log likelihood =  656.44236  
Iteration 2:   log likelihood =  656.54047  
Iteration 3:   log likelihood =  656.54651  
Iteration 4:   log likelihood =  656.54803  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  656.54846  
Iteration 6:   log likelihood =  656.54859  
Iteration 7:   log likelihood =  656.54859  

ARCH family regression

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  656.5486                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 ppg_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ppg_returns  |
       _cons |   .0002439   .0011211     0.22   0.828    -.0019534    .0024413
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L2. |   .2357376   .0854665     2.76   0.006     .0682264    .4032488
         L7. |   .1683802   .0745649     2.26   0.024     .0222357    .3145247
             |
       _cons |   .0002073   .0000199    10.43   0.000     .0001684    .0002463
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    42.0772
 Prob > chi2(40)           =     0.3811

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    50.1643
 Prob > chi2(40)           =     0.1302

.           
.           eststo: arch ppg_returns, arch(2,7) het(bmonday debt csanction)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  665.56298  
Iteration 1:   log likelihood =  665.65545  
Iteration 2:   log likelihood =  669.82902  
Iteration 3:   log likelihood =  670.02775  
Iteration 4:   log likelihood =  670.03209  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  670.03352  
Iteration 6:   log likelihood =  670.03609  
Iteration 7:   log likelihood =   670.0361  

ARCH family regression -- multiplicative heteroskedasticity

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  670.0361                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 ppg_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ppg_returns  |
       _cons |    .000079   .0010058     0.08   0.937    -.0018924    .0020504
-------------+----------------------------------------------------------------
HET          |
     bmonday |   1.395311   .8161845     1.71   0.087    -.2043817    2.995003
        debt |  -.4920698   .3412494    -1.44   0.149    -1.160906    .1767667
   csanction |   .7341529    .264563     2.77   0.006      .215619    1.252687
       _cons |  -8.654479   .1465848   -59.04   0.000     -8.94178   -8.367178
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L2. |   .1472085   .0719891     2.04   0.041     .0061125    .2883045
         L7. |   .1676972   .0836981     2.00   0.045      .003652    .3317423
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    39.1595
 Prob > chi2(40)           =     0.5079

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    30.5897
 Prob > chi2(40)           =     0.8581

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant csanction Sanction debt DebtDe
> bate bmonday BlackMonday) nomtitles title(PPG 2011) nodep  

PPG 2011
----------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)   
----------------------------------------------------------------------------------------
ppg_retu~s                                                                              
Constant      -0.000       -0.000        0.001        0.001        0.000        0.000   
             (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)   
----------------------------------------------------------------------------------------
ARMA                                                                                    
L.ar          -0.043       -0.877***     0.021                                          
             (0.049)      (0.096)      (0.046)                                          
L.ma                        0.806***                                                    
                          (0.116)                                                       
----------------------------------------------------------------------------------------
SIGMA2                                                                                  
Constant       0.000***     0.000***                                                    
             (0.000)      (0.000)                                                       
----------------------------------------------------------------------------------------
ARCH                                                                                    
L2.arch                                  0.297***     0.291***     0.236***     0.147** 
                                       (0.095)      (0.094)      (0.085)      (0.072)   
L7.arch                                                            0.168**      0.168** 
                                                                 (0.075)      (0.084)   
Constant                                 0.000***     0.000***     0.000***             
                                       (0.000)      (0.000)      (0.000)                
----------------------------------------------------------------------------------------
HET                                                                                     
BlackMon~y                                                                      1.395*  
                                                                              (0.816)   
DebtDebate                                                                     -0.492   
                                                                              (0.341)   
Sanction                                                                        0.734***
                                                                              (0.265)   
Constant                                                                       -8.654***
                                                                              (0.147)   
----------------------------------------------------------------------------------------
N                252          252          252          252          252          252   
aic        -1273.568    -1276.575    -1294.794    -1296.687    -1305.097    -1326.072   
bic        -1262.980    -1262.457    -1280.677    -1286.099    -1290.979    -1301.366   
----------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/ppg2011.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant cs
> anction Sanction debt DebtDebate bmonday BlackMonday) nomtitles title(PPG 2011) nodep replace
(note: file rawtables/ppg2011.tex not found)
(output written to rawtables/ppg2011.tex)

.           
.   * Walmart (1989)
.   
.     * Clear
.           clear

.           
.         * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen wmt_returns = ln(wmt_close/wmt_close[_n-1])
(4,473 missing values generated)

.     
.         * Sanctions
.           gen tsanction3 = 1 if date > td("04jun1989") & date < td("25may1990")
(12,847 missing values generated)

.       recode tsanction3(.=0)
(tsanction3: 12847 changes made)

. 
.         * Bond Market Crash    
.           gen f13minicrash = 1 if date > td("11oct1989") & date < td("17oct1989")
(13,091 missing values generated)

.           recode f13minicrash(.=0)
(f13minicrash: 13091 changes made)

.           
.         * Limit Time Period
.       keep if date > td("30dec1988") & date < td("01jun1990")
(12,737 observations deleted)

.                 
.     * Set for analysis
.           tsset t
        time variable:  t, 5278 to 5634
                delta:  1 unit

.           
.         * Table A.13 Models  
.       eststo clear      

.   
.           eststo: arch wmt_returns, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  981.78272  
Iteration 1:   log likelihood =  981.92914  
Iteration 2:   log likelihood =  981.94856  
Iteration 3:   log likelihood =  981.95366  
Iteration 4:   log likelihood =  981.95469  
(switching optimization to BFGS)
Iteration 5:   log likelihood =   981.9549  
Iteration 6:   log likelihood =  981.95497  

Time-series regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       8.38
Log likelihood =   981.955                        Prob > chi2     =     0.0038

------------------------------------------------------------------------------
             |                 OPG
 wmt_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
wmt_returns  |
       _cons |   .0016525   .0007498     2.20   0.028     .0001829    .0031222
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.1129194   .0390122    -2.89   0.004     -.189382   -.0364569
-------------+----------------------------------------------------------------
     /SIGMA2 |    .000239   .0000125    19.12   0.000     .0002145    .0002635
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    49.2690
 Prob > chi2(40)           =     0.1494

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    39.9040
 Prob > chi2(40)           =     0.4745

.           
.           eststo: arch wmt_returns, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  981.74023  
Iteration 1:   log likelihood =  981.92669  
Iteration 2:   log likelihood =  981.94575  
Iteration 3:   log likelihood =  981.94946  
Iteration 4:   log likelihood =  981.95013  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  981.95478  
Iteration 6:   log likelihood =  981.95551  
BFGS stepping has contracted, resetting BFGS Hessian (0)
Iteration 7:   log likelihood =  981.95553  
Iteration 8:   log likelihood =  981.95553  (backed up)
Iteration 9:   log likelihood =  981.95553  (backed up)
Iteration 10:  log likelihood =  981.95553  

Time-series regression -- ARMA disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(2)    =       8.41
Log likelihood =  981.9555                        Prob > chi2     =     0.0150

------------------------------------------------------------------------------
             |                 OPG
 wmt_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
wmt_returns  |
       _cons |   .0016529    .000749     2.21   0.027     .0001849    .0031209
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.1012252    .528142    -0.19   0.848    -1.136364     .933914
             |
          ma |
         L1. |  -.0119323    .527893    -0.02   0.982    -1.046584    1.022719
-------------+----------------------------------------------------------------
     /SIGMA2 |    .000239   .0000129    18.59   0.000     .0002138    .0002642
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    49.3080
 Prob > chi2(40)           =     0.1485

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    39.8717
 Prob > chi2(40)           =     0.4760

.           
.           eststo: arch wmt_returns, ar(1) arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  979.32148  
Iteration 1:   log likelihood =  982.76542  
Iteration 2:   log likelihood =  985.20069  
Iteration 3:   log likelihood =  985.49659  
Iteration 4:   log likelihood =  985.60338  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  985.63347  
Iteration 6:   log likelihood =  985.65156  
Iteration 7:   log likelihood =  985.65165  

ARCH family regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       1.07
Log likelihood =  985.6516                        Prob > chi2     =     0.3001

------------------------------------------------------------------------------
             |                 OPG
 wmt_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
wmt_returns  |
       _cons |   .0015927   .0007663     2.08   0.038     .0000908    .0030946
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.0661836   .0638727    -1.04   0.300    -.1913718    .0590046
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0754176   .0455809     1.65   0.098    -.0139194    .1647546
             |
       _cons |    .000218   .0000115    18.95   0.000     .0001954    .0002405
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    50.8593
 Prob > chi2(40)           =     0.1166

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    19.4506
 Prob > chi2(40)           =     0.9975

.           
.           eststo: arch wmt_returns, ar(1) ma(1) arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  979.08283  
Iteration 1:   log likelihood =  982.83578  
Iteration 2:   log likelihood =  985.18799  
Iteration 3:   log likelihood =  985.49304  
Iteration 4:   log likelihood =   985.5449  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  985.55982  
Iteration 6:   log likelihood =  985.63125  
Iteration 7:   log likelihood =  985.64815  
BFGS stepping has contracted, resetting BFGS Hessian (0)
Iteration 8:   log likelihood =  985.65308  
Iteration 9:   log likelihood =  985.65309  (backed up)
Iteration 10:  log likelihood =  985.65309  (backed up)
Iteration 11:  log likelihood =  985.65313  (backed up)
Iteration 12:  log likelihood =  985.65313  (backed up)
Iteration 13:  log likelihood =  985.65319  
Iteration 14:  log likelihood =   985.6532  

ARCH family regression -- ARMA disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(2)    =       1.07
Log likelihood =  985.6532                        Prob > chi2     =     0.5858

------------------------------------------------------------------------------
             |                 OPG
 wmt_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
wmt_returns  |
       _cons |   .0015932   .0007665     2.08   0.038      .000091    .0030955
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.0365295   .9140098    -0.04   0.968    -1.827956    1.754897
             |
          ma |
         L1. |  -.0298419   .9165677    -0.03   0.974    -1.826282    1.766598
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0754008   .0458156     1.65   0.100    -.0143962    .1651977
             |
       _cons |    .000218   .0000121    18.02   0.000     .0001943    .0002417
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    50.9303
 Prob > chi2(40)           =     0.1153

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    19.4245
 Prob > chi2(40)           =     0.9975

.           
.           eststo: arch wmt_returns, ar(1) arch(1) het(tsanction3 f13minicrash)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  999.40131  
Iteration 1:   log likelihood =  1000.9772  
Iteration 2:   log likelihood =  1001.0058  
Iteration 3:   log likelihood =  1001.0068  
Iteration 4:   log likelihood =  1001.0068  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1001.0071  
Iteration 6:   log likelihood =  1001.0072  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       0.69
Log likelihood =  1001.007                        Prob > chi2     =     0.4074

------------------------------------------------------------------------------
             |                 OPG
 wmt_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
wmt_returns  |
       _cons |   .0017475   .0007403     2.36   0.018     .0002964    .0031985
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.0462915   .0558789    -0.83   0.407    -.1558121    .0632292
-------------+----------------------------------------------------------------
HET          |
  tsanction3 |   .0694339   .1619291     0.43   0.668    -.2479412    .3868091
f13minicrash |   2.886323   1.437274     2.01   0.045     .0693188    5.703328
       _cons |  -8.552926   .1416545   -60.38   0.000    -8.830564   -8.275289
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |     .03408   .0589367     0.58   0.563    -.0814338    .1495937
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    51.9991
 Prob > chi2(40)           =     0.0968

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.5007
 Prob > chi2(40)           =     0.5833

.           
.           eststo: arch wmt_returns, arch(1) het(tsanction3 f13minicrash)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  998.88079  
Iteration 1:   log likelihood =  1000.2807  
Iteration 2:   log likelihood =  1000.6468  
Iteration 3:   log likelihood =  1000.6479  
Iteration 4:   log likelihood =  1000.6512  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1000.6516  
Iteration 6:   log likelihood =  1000.6519  
Iteration 7:   log likelihood =  1000.6519  

ARCH family regression -- multiplicative heteroskedasticity

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  1000.652                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 wmt_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
wmt_returns  |
       _cons |   .0017513   .0007701     2.27   0.023     .0002419    .0032608
-------------+----------------------------------------------------------------
HET          |
  tsanction3 |   .0748199   .1608841     0.47   0.642    -.2405071    .3901469
f13minicrash |   2.928832   1.484912     1.97   0.049     .0184587    5.839205
       _cons |  -8.563549   .1397821   -61.26   0.000    -8.837516   -8.289581
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0424508   .0575714     0.74   0.461    -.0703871    .1552887
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    53.5624
 Prob > chi2(40)           =     0.0742

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    36.7469
 Prob > chi2(40)           =     0.6175

.           
.           eststo: arch wmt_returns, ar(17) arch(1) het(tsanction3 f13minicrash)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1001.7866  
Iteration 1:   log likelihood =   1003.007  
Iteration 2:   log likelihood =  1003.0572  
Iteration 3:   log likelihood =   1003.086  
Iteration 4:   log likelihood =  1003.1259  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1003.1263  
Iteration 6:   log likelihood =  1003.1264  
Iteration 7:   log likelihood =  1003.1264  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       5.20
Log likelihood =  1003.126                        Prob > chi2     =     0.0226

------------------------------------------------------------------------------
             |                 OPG
 wmt_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
wmt_returns  |
       _cons |   .0017387   .0006978     2.49   0.013     .0003711    .0031064
-------------+----------------------------------------------------------------
ARMA         |
          ar |
        L17. |  -.1124625   .0493208    -2.28   0.023    -.2091296   -.0157955
-------------+----------------------------------------------------------------
HET          |
  tsanction3 |   .0941252   .1650072     0.57   0.568     -.229283    .4175334
f13minicrash |   2.952297   1.482595     1.99   0.046     .0464649     5.85813
       _cons |  -8.596096   .1416808   -60.67   0.000    -8.873786   -8.318407
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0475961   .0619234     0.77   0.442    -.0737715    .1689638
------------------------------------------------------------------------------
(est7 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    50.5292
 Prob > chi2(40)           =     0.1229

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    39.1891
 Prob > chi2(40)           =     0.5066

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant tsanction3 Sanction f13minicra
> sh MiniCrash) nomtitles title(Walmart 1989) nodep

Walmart 1989
-----------------------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)          (7)   
-----------------------------------------------------------------------------------------------------
wmt_retu~s                                                                                           
Constant       0.002**      0.002**      0.002**      0.002**      0.002**      0.002**      0.002** 
             (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)   
-----------------------------------------------------------------------------------------------------
ARMA                                                                                                 
L.ar          -0.113***    -0.101       -0.066       -0.037       -0.046                             
             (0.039)      (0.528)      (0.064)      (0.914)      (0.056)                             
L17.ar                                                                                      -0.112** 
                                                                                           (0.049)   
L.ma                       -0.012                    -0.030                                          
                          (0.528)                   (0.917)                                          
-----------------------------------------------------------------------------------------------------
SIGMA2                                                                                               
Constant       0.000***     0.000***                                                                 
             (0.000)      (0.000)                                                                    
-----------------------------------------------------------------------------------------------------
ARCH                                                                                                 
L.arch                                   0.075*       0.075*       0.034        0.042        0.048   
                                       (0.046)      (0.046)      (0.059)      (0.058)      (0.062)   
Constant                                 0.000***     0.000***                                       
                                       (0.000)      (0.000)                                          
-----------------------------------------------------------------------------------------------------
HET                                                                                                  
Sanction                                                           0.069        0.075        0.094   
                                                                 (0.162)      (0.161)      (0.165)   
MiniCrash                                                          2.886**      2.929**      2.952** 
                                                                 (1.437)      (1.485)      (1.483)   
Constant                                                          -8.553***    -8.564***    -8.596***
                                                                 (0.142)      (0.140)      (0.142)   
-----------------------------------------------------------------------------------------------------
N                357          357          357          357          357          357          357   
aic        -1957.910    -1955.911    -1963.303    -1961.306    -1990.014    -1991.304    -1994.253   
bic        -1946.277    -1940.400    -1947.792    -1941.918    -1966.748    -1971.915    -1970.986   
-----------------------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.       esttab using rawtables/walmart1989.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant ts
> anction3 Sanction f13minicrash MiniCrash) nomtitles title(Walmart 1989) nodep replace
(note: file rawtables/walmart1989.tex not found)
(output written to rawtables/walmart1989.tex)

. 
.   * Walmart (2011)
.         
.         * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen wmt_returns = ln(wmt_close/wmt_close[_n-1])
(4,473 missing values generated)

.           
.     * Currency Sanctions
.       gen csanction = 1 if date > td("11jul2011") & date < td("18oct2011")
(13,025 missing values generated)

.       recode csanction(.=0)
(csanction: 13025 changes made)

. 
.     * Debt Debate
.       gen debt = 1 if date > td("13may2011") & date < td("01aug2011")
(13,041 missing values generated)

.       recode debt(.=0)
(debt: 13041 changes made)

. 
.     * Black Mondayd             
.       gen bmonday = 1 if date > td("01aug2011") & date < td("15aug2011")
(13,085 missing values generated)

.       recode bmonday(.=0)
(bmonday: 13085 changes made)

. 
.     * Limit Time Period
.       keep if date > td("31dec2010") & date < td("03jan2012")   
(12,842 observations deleted)

.                 
.     * Set for analysis
.           tsset t
        time variable:  t, 10825 to 11076
                delta:  1 unit

.           
.         * Table A.14 Models  
.       eststo clear      

.           
.           eststo: arch wmt_returns, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  792.77271  
Iteration 1:   log likelihood =  793.12856  
Iteration 2:   log likelihood =  793.17349  
Iteration 3:   log likelihood =  793.18509  
Iteration 4:   log likelihood =  793.18856  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  793.18963  
Iteration 6:   log likelihood =  793.18994  
Iteration 7:   log likelihood =  793.18994  

Time-series regression -- AR disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =      17.11
Log likelihood =  793.1899                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
 wmt_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
wmt_returns  |
       _cons |   .0004042   .0005627     0.72   0.473    -.0006987    .0015071
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.1771941   .0428355    -4.14   0.000    -.2611501   -.0932381
-------------+----------------------------------------------------------------
     /SIGMA2 |    .000108   7.50e-06    14.41   0.000     .0000933    .0001227
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    32.4452
 Prob > chi2(40)           =     0.7964

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    82.3858
 Prob > chi2(40)           =     0.0001

.           
.           eststo: arch wmt_returns, ar(1) arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  800.13967  
Iteration 1:   log likelihood =  803.36991  
Iteration 2:   log likelihood =  803.72517  
Iteration 3:   log likelihood =  803.76542  
Iteration 4:   log likelihood =  803.77204  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  803.77315  
Iteration 6:   log likelihood =  803.77331  
Iteration 7:   log likelihood =  803.77332  

ARCH family regression -- AR disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       0.02
Log likelihood =  803.7733                        Prob > chi2     =     0.8976

------------------------------------------------------------------------------
             |                 OPG
 wmt_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
wmt_returns  |
       _cons |   .0004095   .0006077     0.67   0.500    -.0007816    .0016007
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.0105065   .0816112    -0.13   0.898    -.1704615    .1494486
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .2555578    .085748     2.98   0.003     .0874947    .4236209
             |
       _cons |   .0000788   7.61e-06    10.36   0.000     .0000639    .0000937
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    28.5824
 Prob > chi2(40)           =     0.9110

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    32.6540
 Prob > chi2(40)           =     0.7887

.           
.           eststo: arch wmt_returns, arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  801.95968  
Iteration 1:   log likelihood =  803.50019  
Iteration 2:   log likelihood =   803.7378  
Iteration 3:   log likelihood =  803.76164  
Iteration 4:   log likelihood =  803.76425  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  803.76459  
Iteration 6:   log likelihood =  803.76464  

ARCH family regression

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  803.7646                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 wmt_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
wmt_returns  |
       _cons |   .0004056   .0006127     0.66   0.508    -.0007953    .0016065
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .2584761   .0857442     3.01   0.003     .0904205    .4265316
             |
       _cons |   .0000786   7.50e-06    10.48   0.000     .0000639    .0000933
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    28.6804
 Prob > chi2(40)           =     0.9087

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    32.2539
 Prob > chi2(40)           =     0.8033

.           
.           eststo: arch wmt_returns, arch(1) het(bmonday debt csanction)

(setting optimization to BHHH)
Iteration 0:   log likelihood =   814.4752  
Iteration 1:   log likelihood =  816.69123  
Iteration 2:   log likelihood =  816.76713  
Iteration 3:   log likelihood =  816.77042  
Iteration 4:   log likelihood =  816.77063  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  816.77155  
Iteration 6:   log likelihood =  816.77158  

ARCH family regression -- multiplicative heteroskedasticity

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  816.7716                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 wmt_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
wmt_returns  |
       _cons |   .0004627   .0006047     0.77   0.444    -.0007224    .0016478
-------------+----------------------------------------------------------------
HET          |
     bmonday |   1.809839   .7686486     2.35   0.019     .3033156    3.316363
        debt |  -.4815465   .2271831    -2.12   0.034    -.9268171   -.0362758
   csanction |   .4256476   .1954298     2.18   0.029     .0426123    .8086829
       _cons |  -9.456295   .1297905   -72.86   0.000     -9.71068   -9.201911
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0580027   .0778735     0.74   0.456    -.0946266    .2106321
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    29.8842
 Prob > chi2(40)           =     0.8784

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    13.2055
 Prob > chi2(40)           =     1.0000

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant csanction Sanction debt DebtDe
> bate bmonday BlackMonday) nomtitles title(Walmart 2011) nodep  

Walmart 2011
--------------------------------------------------------------
                 (1)          (2)          (3)          (4)   
--------------------------------------------------------------
wmt_retu~s                                                    
Constant       0.000        0.000        0.000        0.000   
             (0.001)      (0.001)      (0.001)      (0.001)   
--------------------------------------------------------------
ARMA                                                          
L.ar          -0.177***    -0.011                             
             (0.043)      (0.082)                             
--------------------------------------------------------------
SIGMA2                                                        
Constant       0.000***                                       
             (0.000)                                          
--------------------------------------------------------------
ARCH                                                          
L.arch                      0.256***     0.258***     0.058   
                          (0.086)      (0.086)      (0.078)   
Constant                    0.000***     0.000***             
                          (0.000)      (0.000)                
--------------------------------------------------------------
HET                                                           
BlackMon~y                                            1.810** 
                                                    (0.769)   
DebtDebate                                           -0.482** 
                                                    (0.227)   
Sanction                                              0.426** 
                                                    (0.195)   
Constant                                             -9.456***
                                                    (0.130)   
--------------------------------------------------------------
N                252          252          252          252   
aic        -1580.380    -1599.547    -1601.529    -1621.543   
bic        -1569.792    -1585.429    -1590.941    -1600.367   
--------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/walmart2011.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constan
> t csanction Sanction debt DebtDebate bmonday BlackMonday) nomtitles title(Walmart 2011) nodep replace
(note: file rawtables/walmart2011.tex not found)
(output written to rawtables/walmart2011.tex)

.           
.   * Duke (1989)
.   
.     * Clear
.           clear

.           
.         * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen duke_returns = ln(duke_close/duke_close[_n-1])
(4,618 missing values generated)

.     
.         * Sanctions
.           gen tsanction3 = 1 if date > td("04jun1989") & date < td("25may1990")
(12,847 missing values generated)

.       recode tsanction3(.=0)
(tsanction3: 12847 changes made)

. 
.         * Bond Market Crash    
.           gen f13minicrash = 1 if date > td("11oct1989") & date < td("17oct1989")
(13,091 missing values generated)

.           recode f13minicrash(.=0)
(f13minicrash: 13091 changes made)

.           
.         * Limit Time Period
.       keep if date > td("30dec1988") & date < td("01jun1990")
(12,737 observations deleted)

.                 
.     * Set for analysis
.           tsset t
        time variable:  t, 5278 to 5634
                delta:  1 unit

.           
.         * Table A.15 Models  
.       eststo clear      

.   
.           eststo: arch duke_returns, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1229.7077  
Iteration 1:   log likelihood =  1229.8785  
Iteration 2:   log likelihood =  1229.9108  
Iteration 3:   log likelihood =  1229.9204  
Iteration 4:   log likelihood =  1229.9239  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1229.9247  
Iteration 6:   log likelihood =   1229.925  
Iteration 7:   log likelihood =   1229.925  

Time-series regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       0.95
Log likelihood =  1229.925                        Prob > chi2     =     0.3295

------------------------------------------------------------------------------
             |                 OPG
duke_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
duke_returns |
       _cons |   .0004932   .0003972     1.24   0.214    -.0002854    .0012717
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.0402569   .0412881    -0.98   0.330    -.1211801    .0406664
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0000596   3.02e-06    19.71   0.000     .0000537    .0000655
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    31.7001
 Prob > chi2(40)           =     0.8226

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    91.5167
 Prob > chi2(40)           =     0.0000

.           
.           eststo: arch duke_returns, ar(9)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1235.4962  
Iteration 1:   log likelihood =  1235.5178  
Iteration 2:   log likelihood =   1235.523  
Iteration 3:   log likelihood =  1235.5244  
Iteration 4:   log likelihood =  1235.5248  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1235.5249  
Iteration 6:   log likelihood =  1235.5249  

Time-series regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =      18.26
Log likelihood =  1235.525                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
duke_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
duke_returns |
       _cons |   .0005068   .0003484     1.45   0.146    -.0001761    .0011898
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L9. |  -.1809223   .0423444    -4.27   0.000    -.2639158   -.0979287
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0000577   2.94e-06    19.61   0.000      .000052    .0000635
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    21.1247
 Prob > chi2(40)           =     0.9938

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    72.1645
 Prob > chi2(40)           =     0.0014

.           
.           eststo: arch duke_returns, ar(9) arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1238.5001  
Iteration 1:   log likelihood =  1238.9135  
Iteration 2:   log likelihood =  1238.9149  
Iteration 3:   log likelihood =   1238.915  
Iteration 4:   log likelihood =   1238.915  

ARCH family regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       7.63
Log likelihood =  1238.915                        Prob > chi2     =     0.0058

------------------------------------------------------------------------------
             |                 OPG
duke_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
duke_returns |
       _cons |   .0004866   .0003394     1.43   0.152    -.0001786    .0011517
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L9. |  -.1405796   .0509061    -2.76   0.006    -.2403537   -.0408055
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |     .10381   .0461873     2.25   0.025     .0132846    .1943354
             |
       _cons |   .0000516   3.39e-06    15.23   0.000     .0000449    .0000582
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    21.8419
 Prob > chi2(40)           =     0.9914

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    59.4586
 Prob > chi2(40)           =     0.0244

.           
.           eststo: arch duke_returns, ar(9) arch(1,8)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1241.5031  
Iteration 1:   log likelihood =   1244.015  
Iteration 2:   log likelihood =  1244.7293  
Iteration 3:   log likelihood =  1244.7884  
Iteration 4:   log likelihood =  1244.7929  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1244.7941  
BFGS stepping has contracted, resetting BFGS Hessian (0)
Iteration 6:   log likelihood =  1244.7947  
Iteration 7:   log likelihood =  1244.7947  (backed up)
Iteration 8:   log likelihood =  1244.7947  (backed up)
Iteration 9:   log likelihood =  1244.7947  (backed up)
Iteration 10:  log likelihood =  1244.7947  

ARCH family regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       6.61
Log likelihood =  1244.795                        Prob > chi2     =     0.0101

------------------------------------------------------------------------------
             |                 OPG
duke_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
duke_returns |
       _cons |   .0005034   .0003446     1.46   0.144     -.000172    .0011787
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L9. |  -.1286804   .0500366    -2.57   0.010    -.2267503   -.0306105
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1104346   .0469539     2.35   0.019     .0184067    .2024626
         L8. |   .0985566   .0436109     2.26   0.024     .0130807    .1840324
             |
       _cons |   .0000445   3.76e-06    11.83   0.000     .0000371    .0000519
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    22.3547
 Prob > chi2(40)           =     0.9891

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    30.9898
 Prob > chi2(40)           =     0.8458

.           
.           eststo: arch duke_returns, ar(9) arch(1,8) het(tsanction3 f13minicrash)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1242.0767  
Iteration 1:   log likelihood =  1244.3969  
Iteration 2:   log likelihood =  1245.1674  
Iteration 3:   log likelihood =  1245.1966  
Iteration 4:   log likelihood =  1245.1966  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1245.2009  
Iteration 6:   log likelihood =   1245.202  
Iteration 7:   log likelihood =  1245.2021  
Iteration 8:   log likelihood =  1245.2021  
Iteration 9:   log likelihood =  1245.2021  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       6.66
Log likelihood =  1245.202                        Prob > chi2     =     0.0099

------------------------------------------------------------------------------
             |                 OPG
duke_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
duke_returns |
       _cons |    .000505   .0003475     1.45   0.146     -.000176    .0011861
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L9. |   -.129353   .0501177    -2.58   0.010    -.2275819   -.0311242
-------------+----------------------------------------------------------------
HET          |
  tsanction3 |  -.1760556   .1572338    -1.12   0.263    -.4842283     .132117
f13minicrash |   .1425983   1.580097     0.09   0.928    -2.954335    3.239531
       _cons |  -9.921624   .1157689   -85.70   0.000    -10.14853   -9.694721
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1273376   .0540203     2.36   0.018     .0214597    .2332155
         L8. |   .1006493   .0444478     2.26   0.024     .0135332    .1877655
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    22.5858
 Prob > chi2(40)           =     0.9880

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    30.3451
 Prob > chi2(40)           =     0.8654

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant tsanction3 Sanction f13minicra
> sh MiniCrash) nomtitles title(Duke 1989) nodep

Duke 1989
---------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)   
---------------------------------------------------------------------------
duke_ret~s                                                                 
Constant       0.000        0.001        0.000        0.001        0.001   
             (0.000)      (0.000)      (0.000)      (0.000)      (0.000)   
---------------------------------------------------------------------------
ARMA                                                                       
L.ar          -0.040                                                       
             (0.041)                                                       
L9.ar                      -0.181***    -0.141***    -0.129**     -0.129***
                          (0.042)      (0.051)      (0.050)      (0.050)   
---------------------------------------------------------------------------
SIGMA2                                                                     
Constant       0.000***     0.000***                                       
             (0.000)      (0.000)                                          
---------------------------------------------------------------------------
ARCH                                                                       
L.arch                                   0.104**      0.110**      0.127** 
                                       (0.046)      (0.047)      (0.054)   
L8.arch                                               0.099**      0.101** 
                                                    (0.044)      (0.044)   
Constant                                 0.000***     0.000***             
                                       (0.000)      (0.000)                
---------------------------------------------------------------------------
HET                                                                        
Sanction                                                          -0.176   
                                                                 (0.157)   
MiniCrash                                                          0.143   
                                                                 (1.580)   
Constant                                                          -9.922***
                                                                 (0.116)   
---------------------------------------------------------------------------
N                357          357          357          357          357   
aic        -2453.850    -2465.050    -2469.830    -2479.589    -2476.404   
bic        -2442.217    -2453.417    -2454.319    -2460.201    -2449.260   
---------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/duke1989.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant t
> sanction3 Sanction f13minicrash MiniCrash) nomtitles title(Duke 1989) nodep replace
(note: file rawtables/duke1989.tex not found)
(output written to rawtables/duke1989.tex)

.           
.   * Duke (2011)  
.   
.         * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen duke_returns = ln(duke_close/duke_close[_n-1])
(4,618 missing values generated)

.           
.     * Currency Sanctions
.       gen csanction = 1 if date > td("11jul2011") & date < td("18oct2011")
(13,025 missing values generated)

.       recode csanction(.=0)
(csanction: 13025 changes made)

. 
.     * Debt Debate
.       gen debt = 1 if date > td("13may2011") & date < td("01aug2011")
(13,041 missing values generated)

.       recode debt(.=0)
(debt: 13041 changes made)

. 
.     * Black Mondayd             
.       gen bmonday = 1 if date > td("01aug2011") & date < td("15aug2011")
(13,085 missing values generated)

.       recode bmonday(.=0)
(bmonday: 13085 changes made)

. 
.     * Limit Time Period
.       keep if date > td("31dec2010") & date < td("03jan2012")   
(12,842 observations deleted)

.                 
.     * Set for analysis
.           tsset t
        time variable:  t, 10825 to 11076
                delta:  1 unit

.           
.         * Table A.16 Models  
.       eststo clear      

.           
.           eststo: arch duke_returns, ar(1) 

(setting optimization to BHHH)
Iteration 0:   log likelihood =  807.38211  
Iteration 1:   log likelihood =  807.64401  
Iteration 2:   log likelihood =  807.66975  
Iteration 3:   log likelihood =  807.67636  
Iteration 4:   log likelihood =   807.6779  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  807.67813  
Iteration 6:   log likelihood =  807.67819  

Time-series regression -- AR disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =      32.69
Log likelihood =  807.6782                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
duke_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
duke_returns |
       _cons |     .00084   .0004978     1.69   0.092    -.0001357    .0018157
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.2578054   .0450916    -5.72   0.000    -.3461832   -.1694276
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0000963   5.91e-06    16.31   0.000     .0000847    .0001079
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    36.2080
 Prob > chi2(40)           =     0.6417

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    75.5495
 Prob > chi2(40)           =     0.0006

.           
.           eststo: arch duke_returns, ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  804.54526  
Iteration 1:   log likelihood =  805.01915  
Iteration 2:   log likelihood =    805.057  
Iteration 3:   log likelihood =  805.05874  
Iteration 4:   log likelihood =  805.05896  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  805.05901  
Iteration 6:   log likelihood =  805.05902  

Time-series regression -- MA disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =      15.43
Log likelihood =   805.059                        Prob > chi2     =     0.0001

------------------------------------------------------------------------------
             |                 OPG
duke_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
duke_returns |
       _cons |   .0008391   .0005202     1.61   0.107    -.0001804    .0018586
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |   -.185552   .0472323    -3.93   0.000    -.2781256   -.0929785
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0000983   5.57e-06    17.65   0.000     .0000874    .0001092
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    43.0992
 Prob > chi2(40)           =     0.3401

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    96.3255
 Prob > chi2(40)           =     0.0000

.           
.           eststo: arch duke_returns, ma(1) arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  809.94014  
Iteration 1:   log likelihood =  811.90349  
Iteration 2:   log likelihood =  812.63571  
Iteration 3:   log likelihood =  812.86332  
Iteration 4:   log likelihood =  812.92723  
(switching optimization to BFGS)
Iteration 5:   log likelihood =     812.93  
Iteration 6:   log likelihood =  813.00069  
Iteration 7:   log likelihood =  813.00502  
Iteration 8:   log likelihood =  813.00535  
Iteration 9:   log likelihood =  813.00541  
Iteration 10:  log likelihood =  813.00542  

ARCH family regression -- MA disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       0.00
Log likelihood =  813.0054                        Prob > chi2     =     0.9586

------------------------------------------------------------------------------
             |                 OPG
duke_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
duke_returns |
       _cons |   .0007992   .0006078     1.31   0.189     -.000392    .0019904
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |   .0042667   .0821724     0.05   0.959    -.1567883    .1653217
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .2663567   .1076531     2.47   0.013     .0553604    .4773529
             |
       _cons |   .0000725   7.45e-06     9.74   0.000     .0000579    .0000871
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.5736
 Prob > chi2(40)           =     0.5800

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.9607
 Prob > chi2(40)           =     0.5624

.           
.           eststo: arch duke_returns, ar(1) arch(9) het(bmonday debt csanction)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  828.00427  
Iteration 1:   log likelihood =  832.36046  
Iteration 2:   log likelihood =  834.62017  
Iteration 3:   log likelihood =  834.72086  
Iteration 4:   log likelihood =  834.73028  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  834.73142  
Iteration 6:   log likelihood =  834.73281  
Iteration 7:   log likelihood =  834.73282  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       3.66
Log likelihood =  834.7328                        Prob > chi2     =     0.0559

------------------------------------------------------------------------------
             |                 OPG
duke_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
duke_returns |
       _cons |   .0006875   .0004673     1.47   0.141    -.0002284    .0016034
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.1186963   .0620814    -1.91   0.056    -.2403737    .0029811
-------------+----------------------------------------------------------------
HET          |
     bmonday |   1.983546   .6638064     2.99   0.003     .6825092    3.284583
        debt |  -.4848978   .2734656    -1.77   0.076     -1.02088    .0510849
   csanction |   .4521842   .2393311     1.89   0.059    -.0168961    .9212645
       _cons |   -9.69775   .1534231   -63.21   0.000    -9.998454   -9.397047
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L9. |   .1276571    .062644     2.04   0.042     .0048771    .2504372
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    23.3455
 Prob > chi2(40)           =     0.9835

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    28.5039
 Prob > chi2(40)           =     0.9127

.           
.           eststo: arch duke_returns, ma(1) arch(9) het(bmonday debt csanction)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  827.89092  
Iteration 1:   log likelihood =  831.19631  
Iteration 2:   log likelihood =  834.26399  
Iteration 3:   log likelihood =  834.39434  
Iteration 4:   log likelihood =   834.4237  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  834.42579  
Iteration 6:   log likelihood =  834.43751  
Iteration 7:   log likelihood =   834.4376  
Iteration 8:   log likelihood =  834.43763  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       2.46
Log likelihood =  834.4376                        Prob > chi2     =     0.1167

------------------------------------------------------------------------------
             |                 OPG
duke_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
duke_returns |
       _cons |   .0006861   .0004706     1.46   0.145    -.0002363    .0016084
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |  -.0987812     .06297    -1.57   0.117    -.2222002    .0246377
-------------+----------------------------------------------------------------
HET          |
     bmonday |   2.016938   .6713854     3.00   0.003     .7010467    3.332829
        debt |  -.4938757   .2710359    -1.82   0.068    -1.025096    .0373448
   csanction |   .4554612   .2364438     1.93   0.054    -.0079601    .9188825
       _cons |  -9.695396   .1540268   -62.95   0.000    -9.997283   -9.393509
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L9. |   .1269037   .0630091     2.01   0.044     .0034081    .2503993
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    23.5605
 Prob > chi2(40)           =     0.9820

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    28.3485
 Prob > chi2(40)           =     0.9162

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant csanction Sanction debt DebtDe
> bate bmonday BlackMonday) nomtitles title(Duke 2011) nodep  

Duke 2011
---------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)   
---------------------------------------------------------------------------
duke_ret~s                                                                 
Constant       0.001*       0.001        0.001        0.001        0.001   
             (0.000)      (0.001)      (0.001)      (0.000)      (0.000)   
---------------------------------------------------------------------------
ARMA                                                                       
L.ar          -0.258***                              -0.119*               
             (0.045)                                (0.062)                
L.ma                       -0.186***     0.004                    -0.099   
                          (0.047)      (0.082)                   (0.063)   
---------------------------------------------------------------------------
SIGMA2                                                                     
Constant       0.000***     0.000***                                       
             (0.000)      (0.000)                                          
---------------------------------------------------------------------------
ARCH                                                                       
L.arch                                   0.266**                           
                                       (0.108)                             
L9.arch                                               0.128**      0.127** 
                                                    (0.063)      (0.063)   
Constant                                 0.000***                          
                                       (0.000)                             
---------------------------------------------------------------------------
HET                                                                        
BlackMon~y                                            1.984***     2.017***
                                                    (0.664)      (0.671)   
DebtDebate                                           -0.485*      -0.494*  
                                                    (0.273)      (0.271)   
Sanction                                              0.452*       0.455*  
                                                    (0.239)      (0.236)   
Constant                                             -9.698***    -9.695***
                                                    (0.153)      (0.154)   
---------------------------------------------------------------------------
N                252          252          252          252          252   
aic        -1609.356    -1604.118    -1618.011    -1655.466    -1654.875   
bic        -1598.768    -1593.530    -1603.893    -1630.760    -1630.169   
---------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/duke2011.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant c
> sanction Sanction debt DebtDebate bmonday BlackMonday) nomtitles title(Duke 2011) nodep replace
(note: file rawtables/duke2011.tex not found)
(output written to rawtables/duke2011.tex)

.           
.   * Deere (1989)
.   
.     * Clear
.           clear

.           
.         * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen de_returns = ln(de_close/de_close[_n-1])
(4,618 missing values generated)

.     
.         * Sanctions
.           gen tsanction3 = 1 if date > td("04jun1989") & date < td("25may1990")
(12,847 missing values generated)

.       recode tsanction3(.=0)
(tsanction3: 12847 changes made)

. 
.         * Bond Market Crash    
.           gen f13minicrash = 1 if date > td("11oct1989") & date < td("17oct1989")
(13,091 missing values generated)

.           recode f13minicrash(.=0)
(f13minicrash: 13091 changes made)

.           
.         * Limit Time Period
.       keep if date > td("30dec1988") & date < td("01jun1990")
(12,737 observations deleted)

.                 
.     * Set for analysis
.           tsset t
        time variable:  t, 5278 to 5634
                delta:  1 unit

.           
.         * Table A.17 Models  
.       eststo clear      

.           
.           eststo: arch de_returns, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1030.4568  
Iteration 1:   log likelihood =  1032.0969  
Iteration 2:   log likelihood =   1032.344  
Iteration 3:   log likelihood =  1032.3924  
Iteration 4:   log likelihood =  1032.4008  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1032.4024  
Iteration 6:   log likelihood =  1032.4028  
Iteration 7:   log likelihood =  1032.4028  

Time-series regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       1.71
Log likelihood =  1032.403                        Prob > chi2     =     0.1912

------------------------------------------------------------------------------
             |                 OPG
  de_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
de_returns   |
       _cons |    .001206   .0008006     1.51   0.132    -.0003631    .0027751
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0786639    .060183     1.31   0.191    -.0392926    .1966204
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0001802   .0000114    15.77   0.000     .0001578    .0002026
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    44.4926
 Prob > chi2(40)           =     0.2882

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    44.3358
 Prob > chi2(40)           =     0.2938

.           
.           eststo: arch de_returns, ar(3)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1032.9941  
Iteration 1:   log likelihood =  1034.4676  
Iteration 2:   log likelihood =  1034.5918  
Iteration 3:   log likelihood =  1034.6082  
Iteration 4:   log likelihood =  1034.6119  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1034.6128  
Iteration 6:   log likelihood =  1034.6131  
Iteration 7:   log likelihood =  1034.6132  

Time-series regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       4.48
Log likelihood =  1034.613                        Prob > chi2     =     0.0343

------------------------------------------------------------------------------
             |                 OPG
  de_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
de_returns   |
       _cons |   .0012204   .0006396     1.91   0.056    -.0000331     .002474
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L3. |   -.136274   .0643735    -2.12   0.034    -.2624438   -.0101042
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0001779   .0000108    16.52   0.000     .0001568    .0001991
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    38.9377
 Prob > chi2(40)           =     0.5180

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    45.6260
 Prob > chi2(40)           =     0.2497

.           
.           eststo: arch de_returns, ar(3) ma(5)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1035.1476  
Iteration 1:   log likelihood =  1036.4229  
Iteration 2:   log likelihood =  1036.6006  
Iteration 3:   log likelihood =  1036.6325  
Iteration 4:   log likelihood =  1036.6406  
(switching optimization to BFGS)
Iteration 5:   log likelihood =   1036.643  
Iteration 6:   log likelihood =   1036.644  
Iteration 7:   log likelihood =   1036.644  

Time-series regression -- ARMA disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(2)    =       7.45
Log likelihood =  1036.644                        Prob > chi2     =     0.0241

------------------------------------------------------------------------------
             |                 OPG
  de_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
de_returns   |
       _cons |    .001226   .0005642     2.17   0.030     .0001202    .0023318
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L3. |  -.1460539   .0655101    -2.23   0.026    -.2744513   -.0176564
             |
          ma |
         L5. |  -.1032504   .0559706    -1.84   0.065    -.2129508      .00645
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0001759   .0000111    15.91   0.000     .0001542    .0001976
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    31.2476
 Prob > chi2(40)           =     0.8376

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    47.4096
 Prob > chi2(40)           =     0.1961

.           
.           eststo: arch de_returns, ar(3,5)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1034.8733  
Iteration 1:   log likelihood =  1036.0905  
Iteration 2:   log likelihood =    1036.25  
Iteration 3:   log likelihood =  1036.2662  
Iteration 4:   log likelihood =   1036.268  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1036.2683  
Iteration 6:   log likelihood =  1036.2684  

Time-series regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(2)    =       6.63
Log likelihood =  1036.268                        Prob > chi2     =     0.0363

------------------------------------------------------------------------------
             |                 OPG
  de_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
de_returns   |
       _cons |   .0012224   .0005879     2.08   0.038     .0000702    .0023746
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L3. |  -.1354346   .0649746    -2.08   0.037    -.2627825   -.0080867
         L5. |  -.0957584   .0554967    -1.73   0.084      -.20453    .0130133
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0001763   .0000111    15.86   0.000     .0001545    .0001981
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    32.2021
 Prob > chi2(40)           =     0.8051

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    47.7532
 Prob > chi2(40)           =     0.1868

.           
.           eststo: arch de_returns, ar(3,5,8)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1037.3958  
Iteration 1:   log likelihood =  1038.3651  
Iteration 2:   log likelihood =  1038.4946  
Iteration 3:   log likelihood =  1038.5138  
Iteration 4:   log likelihood =  1038.5157  
(switching optimization to BFGS)
Iteration 5:   log likelihood =   1038.516  
Iteration 6:   log likelihood =  1038.5161  
Iteration 7:   log likelihood =  1038.5161  

Time-series regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(3)    =      10.18
Log likelihood =  1038.516                        Prob > chi2     =     0.0171

------------------------------------------------------------------------------
             |                 OPG
  de_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
de_returns   |
       _cons |   .0012372   .0005282     2.34   0.019      .000202    .0022724
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L3. |  -.1472862   .0654143    -2.25   0.024    -.2754958   -.0190766
         L5. |  -.1096861   .0557217    -1.97   0.049    -.2188987   -.0004736
         L8. |  -.1144818   .0575448    -1.99   0.047    -.2272675   -.0016961
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0001741    .000011    15.85   0.000     .0001526    .0001956
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    25.6819
 Prob > chi2(40)           =     0.9615

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    44.7100
 Prob > chi2(40)           =     0.2806

.           
.           eststo: arch de_returns, ar(3,5,8) het(tsanction3 f13minicrash)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1037.5207  
Iteration 1:   log likelihood =  1043.8293  
Iteration 2:   log likelihood =  1044.3953  
Iteration 3:   log likelihood =  1044.4427  
Iteration 4:   log likelihood =  1044.4454  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1044.4457  
Iteration 6:   log likelihood =  1044.4458  
Iteration 7:   log likelihood =  1044.4458  

Time-series regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(3)    =      10.48
Log likelihood =  1044.446                        Prob > chi2     =     0.0149

------------------------------------------------------------------------------
             |                 OPG
  de_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
de_returns   |
       _cons |   .0012806   .0005158     2.48   0.013     .0002696    .0022916
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L3. |  -.1468408   .0631254    -2.33   0.020    -.2705643   -.0231173
         L5. |  -.1003547   .0538714    -1.86   0.062    -.2059408    .0052313
         L8. |  -.1140835   .0552829    -2.06   0.039    -.2224359   -.0057311
-------------+----------------------------------------------------------------
HET          |
  tsanction3 |   -.197248   .1541429    -1.28   0.201    -.4993625    .1048664
f13minicrash |   1.929266   .8288853     2.33   0.020     .3046805    3.553851
       _cons |  -8.568874   .1321432   -64.85   0.000     -8.82787   -8.309878
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    25.5163
 Prob > chi2(40)           =     0.9635

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    42.4319
 Prob > chi2(40)           =     0.3666

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant tsanction3 Sanction f13minicra
> sh MiniCrash) nomtitles title(Deere 1989) nodep

Deere 1989
----------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)   
----------------------------------------------------------------------------------------
de_returns                                                                              
Constant       0.001        0.001*       0.001**      0.001**      0.001**      0.001** 
             (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)   
----------------------------------------------------------------------------------------
ARMA                                                                                    
L.ar           0.079                                                                    
             (0.060)                                                                    
L3.ar                      -0.136**     -0.146**     -0.135**     -0.147**     -0.147** 
                          (0.064)      (0.066)      (0.065)      (0.065)      (0.063)   
L5.ar                                                -0.096*      -0.110**     -0.100*  
                                                    (0.055)      (0.056)      (0.054)   
L8.ar                                                             -0.114**     -0.114** 
                                                                 (0.058)      (0.055)   
L5.ma                                   -0.103*                                         
                                       (0.056)                                          
----------------------------------------------------------------------------------------
SIGMA2                                                                                  
Constant       0.000***     0.000***     0.000***     0.000***     0.000***             
             (0.000)      (0.000)      (0.000)      (0.000)      (0.000)                
----------------------------------------------------------------------------------------
HET                                                                                     
Sanction                                                                       -0.197   
                                                                              (0.154)   
MiniCrash                                                                       1.929** 
                                                                              (0.829)   
Constant                                                                       -8.569***
                                                                              (0.132)   
----------------------------------------------------------------------------------------
N                357          357          357          357          357          357   
aic        -2058.806    -2063.226    -2065.288    -2064.537    -2067.032    -2074.892   
bic        -2047.172    -2051.593    -2049.777    -2049.026    -2047.643    -2047.747   
----------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/deere1989.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant 
> tsanction3 Sanction f13minicrash MiniCrash) nomtitles title(Deere 1989) nodep replace
(note: file rawtables/deere1989.tex not found)
(output written to rawtables/deere1989.tex)

.           
.   * Deere (2011)          
.   
.         * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen de_returns = ln(de_close/de_close[_n-1])
(4,618 missing values generated)

.           
.     * Currency Sanctions
.       gen csanction = 1 if date > td("11jul2011") & date < td("18oct2011")
(13,025 missing values generated)

.       recode csanction(.=0)
(csanction: 13025 changes made)

. 
.     * Debt Debate
.       gen debt = 1 if date > td("13may2011") & date < td("01aug2011")
(13,041 missing values generated)

.       recode debt(.=0)
(debt: 13041 changes made)

. 
.     * Black Mondayd             
.       gen bmonday = 1 if date > td("01aug2011") & date < td("15aug2011")
(13,085 missing values generated)

.       recode bmonday(.=0)
(bmonday: 13085 changes made)

. 
.     * Limit Time Period
.       keep if date > td("31dec2010") & date < td("03jan2012")   
(12,842 observations deleted)

.                 
.     * Set for analysis
.           tsset t
        time variable:  t, 10825 to 11076
                delta:  1 unit

.           
.         * Table A.18 Models  
.       eststo clear      

.           
.           eststo: arch de_returns, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  611.19989  
Iteration 1:   log likelihood =   611.7003  
Iteration 2:   log likelihood =   611.7257  
Iteration 3:   log likelihood =  611.72825  
Iteration 4:   log likelihood =  611.72849  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  611.72853  

Time-series regression -- AR disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       0.02
Log likelihood =  611.7285                        Prob > chi2     =     0.8953

------------------------------------------------------------------------------
             |                 OPG
  de_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
de_returns   |
       _cons |  -.0002816   .0013794    -0.20   0.838    -.0029852     .002422
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0067376   .0511768     0.13   0.895     -.093567    .1070422
-------------+----------------------------------------------------------------
     /SIGMA2 |    .000456   .0000326    13.99   0.000     .0003921    .0005199
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    50.1850
 Prob > chi2(40)           =     0.1298

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =   109.4772
 Prob > chi2(40)           =     0.0000

.           
.           eststo: arch de_returns, ar(3,5)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  616.70387  
Iteration 1:   log likelihood =  616.84748  
Iteration 2:   log likelihood =   616.8594  
Iteration 3:   log likelihood =  616.86063  
Iteration 4:   log likelihood =  616.86077  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  616.86078  

Time-series regression -- AR disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =      16.18
Log likelihood =  616.8608                        Prob > chi2     =     0.0003

------------------------------------------------------------------------------
             |                 OPG
  de_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
de_returns   |
       _cons |  -.0002846   .0010484    -0.27   0.786    -.0023394    .0017702
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L3. |  -.1339146   .0529615    -2.53   0.011    -.2377173   -.0301119
         L5. |  -.1397619    .057563    -2.43   0.015    -.2525833   -.0269404
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0004379   .0000342    12.82   0.000     .0003709    .0005048
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    39.7217
 Prob > chi2(40)           =     0.4827

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    92.6805
 Prob > chi2(40)           =     0.0000

.           
.           eststo: arch de_returns, ar(3,5) arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  617.64481  
Iteration 1:   log likelihood =  619.54508  
Iteration 2:   log likelihood =  619.71091  
Iteration 3:   log likelihood =  619.71416  
Iteration 4:   log likelihood =  619.71461  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  619.71476  
Iteration 6:   log likelihood =  619.71486  
Iteration 7:   log likelihood =  619.71486  

ARCH family regression -- AR disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =      10.25
Log likelihood =  619.7149                        Prob > chi2     =     0.0059

------------------------------------------------------------------------------
             |                 OPG
  de_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
de_returns   |
       _cons |  -.0003276   .0010713    -0.31   0.760    -.0024273    .0017722
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L3. |  -.1222972   .0596757    -2.05   0.040    -.2392595   -.0053349
         L5. |  -.1292044   .0546808    -2.36   0.018    -.2363768   -.0220321
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1191782   .0784183     1.52   0.129    -.0345188    .2728752
             |
       _cons |   .0003821   .0000326    11.72   0.000     .0003182    .0004459
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    35.1163
 Prob > chi2(40)           =     0.6895

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    65.2475
 Prob > chi2(40)           =     0.0071

.           
.           eststo: arch de_returns, ar(3,5) arch(1,2)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  623.29528  
Iteration 1:   log likelihood =   626.2287  
Iteration 2:   log likelihood =    626.902  
Iteration 3:   log likelihood =  626.94869  
Iteration 4:   log likelihood =  626.96119  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  626.96358  
Iteration 6:   log likelihood =  626.96431  
Iteration 7:   log likelihood =  626.96431  

ARCH family regression -- AR disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =       7.10
Log likelihood =  626.9643                        Prob > chi2     =     0.0288

------------------------------------------------------------------------------
             |                 OPG
  de_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
de_returns   |
       _cons |   .0004285    .001069     0.40   0.689    -.0016668    .0025237
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L3. |  -.0307776   .0587523    -0.52   0.600      -.14593    .0843748
         L5. |  -.1329439   .0504207    -2.64   0.008    -.2317667   -.0341211
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1042968   .0697649     1.49   0.135    -.0324399    .2410335
         L2. |   .2437959   .0913636     2.67   0.008     .0647265    .4228653
             |
       _cons |   .0002795   .0000406     6.88   0.000     .0001998    .0003591
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    33.7857
 Prob > chi2(40)           =     0.7450

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    46.3192
 Prob > chi2(40)           =     0.2278

.           
.           eststo: arch de_returns, ar(3,5) arch(1) garch(1) 

(setting optimization to BHHH)
Iteration 0:   log likelihood =  617.53394  
Iteration 1:   log likelihood =  620.12994  
Iteration 2:   log likelihood =  624.83161  
Iteration 3:   log likelihood =  627.78734  
Iteration 4:   log likelihood =  628.31428  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  628.42069  
Iteration 6:   log likelihood =  628.45739  
Iteration 7:   log likelihood =  628.45989  
Iteration 8:   log likelihood =     628.46  
Iteration 9:   log likelihood =  628.46001  

ARCH family regression -- AR disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =       2.90
Log likelihood =    628.46                        Prob > chi2     =     0.2346

------------------------------------------------------------------------------
             |                 OPG
  de_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
de_returns   |
       _cons |    .000088   .0011484     0.08   0.939    -.0021628    .0023388
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L3. |  -.0781218   .0764165    -1.02   0.307    -.2278954    .0716517
         L5. |  -.0921311    .069884    -1.32   0.187    -.2291013    .0448391
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0918845   .0336245     2.73   0.006     .0259818    .1577872
             |
       garch |
         L1. |   .8582725    .057659    14.89   0.000      .745263    .9712821
             |
       _cons |    .000022   .0000152     1.45   0.148    -7.79e-06    .0000518
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    31.2497
 Prob > chi2(40)           =     0.8375

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    32.0096
 Prob > chi2(40)           =     0.8119

.           
.           eststo: arch de_returns, ar(3,5) arch(2) garch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  622.50534  
Iteration 1:   log likelihood =  624.42425  
Iteration 2:   log likelihood =  626.79717  
Iteration 3:   log likelihood =  627.72936  
Iteration 4:   log likelihood =  628.12188  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  628.30105  
Iteration 6:   log likelihood =  628.42587  
Iteration 7:   log likelihood =  628.43203  
Iteration 8:   log likelihood =   628.4703  
Iteration 9:   log likelihood =  628.47947  
Iteration 10:  log likelihood =  628.48225  
Iteration 11:  log likelihood =  628.48244  
Iteration 12:  log likelihood =  628.48244  

ARCH family regression -- AR disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =       2.67
Log likelihood =  628.4824                        Prob > chi2     =     0.2637

------------------------------------------------------------------------------
             |                 OPG
  de_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
de_returns   |
       _cons |   .0000448   .0011352     0.04   0.969    -.0021802    .0022698
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L3. |  -.0682724   .0749369    -0.91   0.362     -.215146    .0786012
         L5. |  -.0927042   .0730234    -1.27   0.204    -.2358273     .050419
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L2. |   .1054552   .0340672     3.10   0.002     .0386847    .1722257
             |
       garch |
         L1. |   .8413029    .049353    17.05   0.000     .7445729    .9380329
             |
       _cons |   .0000238   .0000151     1.57   0.116    -5.86e-06    .0000535
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    33.6125
 Prob > chi2(40)           =     0.7519

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    27.0909
 Prob > chi2(40)           =     0.9406

.           
.           eststo: arch de_returns, ar(3,5) arch(1,2,13) 

(setting optimization to BHHH)
Iteration 0:   log likelihood =  625.63285  
Iteration 1:   log likelihood =  628.62481  
Iteration 2:   log likelihood =   629.4847  
Iteration 3:   log likelihood =  629.54215  
Iteration 4:   log likelihood =  629.55239  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  629.55457  
Iteration 6:   log likelihood =  629.55536  
Iteration 7:   log likelihood =  629.55538  

ARCH family regression -- AR disturbances

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =       7.58
Log likelihood =  629.5554                        Prob > chi2     =     0.0226

------------------------------------------------------------------------------
             |                 OPG
  de_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
de_returns   |
       _cons |   .0000193   .0010659     0.02   0.986    -.0020697    .0021084
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L3. |  -.0500891   .0688249    -0.73   0.467    -.1849835    .0848053
         L5. |  -.1371906   .0515169    -2.66   0.008    -.2381619   -.0362193
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0966572   .0630029     1.53   0.125    -.0268262    .2201407
         L2. |   .2094804   .0766065     2.73   0.006     .0593345    .3596263
        L13. |   .1248717   .0813156     1.54   0.125    -.0345039    .2842472
             |
       _cons |   .0002373   .0000421     5.64   0.000     .0001548    .0003199
------------------------------------------------------------------------------
(est7 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    30.5140
 Prob > chi2(40)           =     0.8604

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    29.1220
 Prob > chi2(40)           =     0.8983

.           
.           eststo: arch de_returns, ar(3,5) arch(2) garch(1) het(bmonday debt csanction)

(setting optimization to BHHH)
Iteration 0:   log likelihood =   631.9184  
Iteration 1:   log likelihood =  636.38273  
Iteration 2:   log likelihood =  636.54479  
Iteration 3:   log likelihood =  637.83798  
Iteration 4:   log likelihood =  638.22264  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  638.30298  
Iteration 6:   log likelihood =  638.77228  
Iteration 7:   log likelihood =  638.81791  
Iteration 8:   log likelihood =  638.83678  
Iteration 9:   log likelihood =  638.85798  
Iteration 10:  log likelihood =  638.86218  
Iteration 11:  log likelihood =  638.86249  
Iteration 12:  log likelihood =  638.86273  
Iteration 13:  log likelihood =  638.86274  
Iteration 14:  log likelihood =  638.86274  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =       3.28
Log likelihood =  638.8627                        Prob > chi2     =     0.1945

------------------------------------------------------------------------------
             |                 OPG
  de_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
de_returns   |
       _cons |   .0003852   .0010048     0.38   0.701    -.0015842    .0023547
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L3. |  -.0733191   .0611901    -1.20   0.231    -.1932495    .0466113
         L5. |  -.0722954   .0545388    -1.33   0.185    -.1791895    .0345987
-------------+----------------------------------------------------------------
HET          |
     bmonday |   .7903879   .3534454     2.24   0.025     .0976477    1.483128
        debt |  -.3699796   .2372466    -1.56   0.119    -.8349744    .0950152
   csanction |    .796564   .2385285     3.34   0.001     .3290566    1.264071
       _cons |  -7.529328   .1646628   -45.73   0.000    -7.852061   -7.206595
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L2. |   .0477169   .0502463     0.95   0.342     -.050764    .1461978
             |
       garch |
         L1. |  -.7391391   .1608429    -4.60   0.000    -1.054385   -.4238929
------------------------------------------------------------------------------
(est8 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    31.4393
 Prob > chi2(40)           =     0.8313

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    29.5154
 Prob > chi2(40)           =     0.8883

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant csanction Sanction debt DebtDe
> bate bmonday BlackMonday) nomtitles title(Deere 2011) nodep  

Deere 2011
------------------------------------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)          (7)          (8)   
------------------------------------------------------------------------------------------------------------------
de_returns                                                                                                        
Constant      -0.000       -0.000       -0.000        0.000        0.000        0.000        0.000        0.000   
             (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)   
------------------------------------------------------------------------------------------------------------------
ARMA                                                                                                              
L.ar           0.007                                                                                              
             (0.051)                                                                                              
L3.ar                      -0.134**     -0.122**     -0.031       -0.078       -0.068       -0.050       -0.073   
                          (0.053)      (0.060)      (0.059)      (0.076)      (0.075)      (0.069)      (0.061)   
L5.ar                      -0.140**     -0.129**     -0.133***    -0.092       -0.093       -0.137***    -0.072   
                          (0.058)      (0.055)      (0.050)      (0.070)      (0.073)      (0.052)      (0.055)   
------------------------------------------------------------------------------------------------------------------
SIGMA2                                                                                                            
Constant       0.000***     0.000***                                                                              
             (0.000)      (0.000)                                                                                 
------------------------------------------------------------------------------------------------------------------
ARCH                                                                                                              
L.arch                                   0.119        0.104        0.092***                  0.097                
                                       (0.078)      (0.070)      (0.034)                   (0.063)                
L2.arch                                               0.244***                  0.105***     0.209***     0.048   
                                                    (0.091)                   (0.034)      (0.077)      (0.050)   
L13.arch                                                                                     0.125                
                                                                                           (0.081)                
L.garch                                                            0.858***     0.841***                 -0.739***
                                                                 (0.058)      (0.049)                   (0.161)   
Constant                                 0.000***     0.000***     0.000        0.000        0.000***             
                                       (0.000)      (0.000)      (0.000)      (0.000)      (0.000)                
------------------------------------------------------------------------------------------------------------------
HET                                                                                                               
BlackMon~y                                                                                                0.790** 
                                                                                                        (0.353)   
DebtDebate                                                                                               -0.370   
                                                                                                        (0.237)   
Sanction                                                                                                  0.797***
                                                                                                        (0.239)   
Constant                                                                                                 -7.529***
                                                                                                        (0.165)   
------------------------------------------------------------------------------------------------------------------
N                252          252          252          252          252          252          252          252   
aic        -1217.457    -1225.722    -1229.430    -1241.929    -1244.920    -1244.965    -1245.111    -1259.725   
bic        -1206.869    -1211.604    -1211.783    -1220.752    -1223.743    -1223.788    -1220.405    -1227.961   
------------------------------------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/deere2011.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant 
> csanction Sanction debt DebtDebate bmonday BlackMonday) nomtitles title(Deere 2011) nodep replace
(note: file rawtables/deere2011.tex not found)
(output written to rawtables/deere2011.tex)

.           
.   * Exxon Mobile 1992 (Columbia)
.   
.         * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen xom_returns = ln(xom_close/xom_close[_n-1])
(3,146 missing values generated)

.           
.         * Sanctions Variable
.           gen sanct = 1 if date > td("16jan1992") & date < td("30jan1992")
(13,085 missing values generated)

.           recode sanct(.=0)
(sanct: 13085 changes made)

.   
.           gen sanc = 1 if date > td("29jan1992")
(6,055 missing values generated)

.           recode sanc(.=0)  
(sanc: 6055 changes made)

.           
.     * Limit Time Period
.           keep if date > td("01jun1991") & date < td("01jun1992")
(12,842 observations deleted)

.           
.     * Set for analysis
.           tsset t
        time variable:  t, 5888 to 6139
                delta:  1 unit

.           
.         * Table A.19 Models  
.           eststo clear  

.           
.           eststo: arch xom_returns

(setting optimization to BHHH)
Iteration 0:   log likelihood =  755.38855  
Iteration 1:   log likelihood =  755.38855  

Time-series regression

Sample: 5888 - 6139                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  755.3885                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 xom_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .0001586    .000773     0.21   0.837    -.0013565    .0016737
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0001458   9.42e-06    15.49   0.000     .0001274    .0001643
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.0731
 Prob > chi2(40)           =     0.6028

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    33.4619
 Prob > chi2(40)           =     0.7579

.           
.           eststo: arch xom_returns, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  758.15052  
Iteration 1:   log likelihood =  758.89081  
Iteration 2:   log likelihood =  759.01619  
Iteration 3:   log likelihood =  759.04506  
Iteration 4:   log likelihood =  759.04984  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  759.05068  
Iteration 6:   log likelihood =  759.05093  
Iteration 7:   log likelihood =  759.05093  

Time-series regression -- AR disturbances

Sample: 5888 - 6139                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       9.08
Log likelihood =  759.0509                        Prob > chi2     =     0.0026

------------------------------------------------------------------------------
             |                 OPG
 xom_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
xom_returns  |
       _cons |   .0001664   .0006646     0.25   0.802    -.0011362     .001469
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.1704446   .0565562    -3.01   0.003    -.2812928   -.0595965
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0001417   9.02e-06    15.70   0.000      .000124    .0001593
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    27.8430
 Prob > chi2(40)           =     0.9267

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    25.1419
 Prob > chi2(40)           =     0.9679

.           
.           eststo: arch xom_returns, ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  759.56931  
Iteration 1:   log likelihood =  760.45159  
Iteration 2:   log likelihood =   760.5048  
Iteration 3:   log likelihood =  760.51827  
Iteration 4:   log likelihood =  760.52115  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  760.52211  
Iteration 6:   log likelihood =  760.52239  
Iteration 7:   log likelihood =  760.52239  

Time-series regression -- MA disturbances

Sample: 5888 - 6139                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =      16.24
Log likelihood =  760.5224                        Prob > chi2     =     0.0001

------------------------------------------------------------------------------
             |                 OPG
 xom_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
xom_returns  |
       _cons |   .0001744   .0005823     0.30   0.765    -.0009669    .0013157
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |  -.2448841    .060769    -4.03   0.000    -.3639891   -.1257791
-------------+----------------------------------------------------------------
     /SIGMA2 |     .00014   8.94e-06    15.65   0.000     .0001225    .0001575
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    25.5002
 Prob > chi2(40)           =     0.9637

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    23.6246
 Prob > chi2(40)           =     0.9816

.           
.           eststo: arch xom_returns, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  759.68889  
Iteration 1:   log likelihood =  763.22631  
Iteration 2:   log likelihood =  764.01728  
Iteration 3:   log likelihood =  764.13706  
Iteration 4:   log likelihood =  764.15498  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  764.15869  
Iteration 6:   log likelihood =  764.16072  
Iteration 7:   log likelihood =  764.16117  
Iteration 8:   log likelihood =  764.16119  

Time-series regression -- ARMA disturbances

Sample: 5888 - 6139                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =      62.54
Log likelihood =  764.1612                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
 xom_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
xom_returns  |
       _cons |   .0001824   .0004124     0.44   0.658    -.0006259    .0009906
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |    .487918    .152599     3.20   0.001     .1888295    .7870065
             |
          ma |
         L1. |  -.7229413   .1263776    -5.72   0.000    -.9706369   -.4752457
-------------+----------------------------------------------------------------
     /SIGMA2 |    .000136   8.47e-06    16.05   0.000     .0001194    .0001526
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    23.3144
 Prob > chi2(40)           =     0.9837

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    22.1500
 Prob > chi2(40)           =     0.9901

.           
.           eststo: arch xom_returns, ar(1) ma(1) het(sanc sanct)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  755.13905  
Iteration 1:   log likelihood =  760.19268  
Iteration 2:   log likelihood =  763.70528  
Iteration 3:   log likelihood =  765.09492  
Iteration 4:   log likelihood =  765.32848  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  765.98482  
Iteration 6:   log likelihood =  766.72973  
Iteration 7:   log likelihood =  766.87992  
Iteration 8:   log likelihood =  766.89388  
Iteration 9:   log likelihood =  766.89606  
Iteration 10:  log likelihood =   766.8962  
Iteration 11:  log likelihood =   766.8962  

Time-series regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 5888 - 6139                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =      64.94
Log likelihood =  766.8962                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
 xom_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
xom_returns  |
       _cons |   .0001639   .0003924     0.42   0.676    -.0006051    .0009329
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .5143467   .1532388     3.36   0.001     .2140042    .8146892
             |
          ma |
         L1. |  -.7453789   .1225071    -6.08   0.000    -.9854883   -.5052695
-------------+----------------------------------------------------------------
HET          |
        sanc |   .4350523   .1241618     3.50   0.000     .1916997     .678405
       sanct |   .3389988   .8903953     0.38   0.703    -1.406144    2.084141
       _cons |  -9.081478   .0813592  -111.62   0.000    -9.240939   -8.922016
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    19.9349
 Prob > chi2(40)           =     0.9967

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    23.3161
 Prob > chi2(40)           =     0.9837

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sanc Sanctions sanct SanctionT
> hreats) nomtitles title(Exxon Mobil 1992) nodep  

Exxon Mobil 1992
---------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)   
---------------------------------------------------------------------------
xom_retu~s                                                                 
Constant       0.000        0.000        0.000        0.000        0.000   
             (0.001)      (0.001)      (0.001)      (0.000)      (0.000)   
---------------------------------------------------------------------------
SIGMA2                                                                     
Constant       0.000***     0.000***     0.000***     0.000***             
             (0.000)      (0.000)      (0.000)      (0.000)                
---------------------------------------------------------------------------
ARMA                                                                       
L.ar                       -0.170***                  0.488***     0.514***
                          (0.057)                   (0.153)      (0.153)   
L.ma                                    -0.245***    -0.723***    -0.745***
                                       (0.061)      (0.126)      (0.123)   
---------------------------------------------------------------------------
HET                                                                        
Sanctions                                                          0.435***
                                                                 (0.124)   
Sanction~s                                                         0.339   
                                                                 (0.890)   
Constant                                                          -9.081***
                                                                 (0.081)   
---------------------------------------------------------------------------
N                252          252          252          252          252   
aic        -1506.777    -1512.102    -1515.045    -1520.322    -1521.792   
bic        -1499.718    -1501.514    -1504.456    -1506.205    -1500.616   
---------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/exxon1992.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant 
> sanc Sanctions sanct SanctionThreats) nomtitles title(Exxon Mobil 1992) nodep replace
(note: file rawtables/exxon1992.tex not found)
(output written to rawtables/exxon1992.tex)

.   
.   * Halliburton 1992 (Columbia)
.   
.         * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen hal_returns = ln(hal_close/hal_close[_n-1])
(3,148 missing values generated)

.           
.         * Sanctions Variable
.           gen sanct = 1 if date > td("16jan1992") & date < td("30jan1992")
(13,085 missing values generated)

.           recode sanct(.=0)
(sanct: 13085 changes made)

.   
.           gen sanc = 1 if date > td("29jan1992")
(6,055 missing values generated)

.           recode sanc(.=0)  
(sanc: 6055 changes made)

.           
.     * Limit Time Period
.           keep if date > td("01jun1991") & date < td("01jun1992")
(12,842 observations deleted)

.           
.     * Set for analysis
.           tsset t
        time variable:  t, 5888 to 6139
                delta:  1 unit

.           
.         * Table A.20 Models  
.           eststo clear

.           
.           eststo: arch hal_returns

(setting optimization to BHHH)
Iteration 0:   log likelihood =  588.44589  
Iteration 1:   log likelihood =  588.44589  

Time-series regression

Sample: 5888 - 6139                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  588.4459                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 hal_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |  -.0017325   .0015135    -1.14   0.252    -.0046988    .0012338
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0005486   .0000407    13.48   0.000     .0004689    .0006284
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    32.4748
 Prob > chi2(40)           =     0.7953

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    38.6484
 Prob > chi2(40)           =     0.5311

.           
.           eststo: arch hal_returns, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  587.85646  
Iteration 1:   log likelihood =   588.4344  
Iteration 2:   log likelihood =  588.49166  
Iteration 3:   log likelihood =  588.49347  
Iteration 4:   log likelihood =  588.49356  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  588.49356  

Time-series regression -- AR disturbances

Sample: 5888 - 6139                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       0.12
Log likelihood =  588.4936                        Prob > chi2     =     0.7321

------------------------------------------------------------------------------
             |                 OPG
 hal_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
hal_returns  |
       _cons |  -.0017332   .0015434    -1.12   0.261    -.0047582    .0012919
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |    .019453   .0568292     0.34   0.732    -.0919301    .1308361
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0005484   .0000407    13.48   0.000     .0004687    .0006281
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    31.5890
 Prob > chi2(40)           =     0.8263

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    38.1416
 Prob > chi2(40)           =     0.5542

.           
.           eststo: arch hal_returns, ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  587.68094  
Iteration 1:   log likelihood =  588.42586  
Iteration 2:   log likelihood =  588.48798  
Iteration 3:   log likelihood =  588.49235  
Iteration 4:   log likelihood =  588.49273  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  588.49275  

Time-series regression -- MA disturbances

Sample: 5888 - 6139                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       0.11
Log likelihood =  588.4928                        Prob > chi2     =     0.7346

------------------------------------------------------------------------------
             |                 OPG
 hal_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
hal_returns  |
       _cons |  -.0017339   .0015419    -1.12   0.261     -.004756    .0012882
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |   .0190832   .0562985     0.34   0.735    -.0912598    .1294262
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0005484   .0000407    13.48   0.000     .0004686    .0006281
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    31.6053
 Prob > chi2(40)           =     0.8258

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    38.1560
 Prob > chi2(40)           =     0.5535

.           
.           eststo: arch hal_returns, ar(1) ma(1) arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  587.74839  
Iteration 1:   log likelihood =  589.54096  
Iteration 2:   log likelihood =  589.77963  
Iteration 3:   log likelihood =  589.80557  
Iteration 4:   log likelihood =  589.84602  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  589.84786  
Iteration 6:   log likelihood =  589.87371  
Iteration 7:   log likelihood =  589.87478  
Iteration 8:   log likelihood =  589.87493  
Iteration 9:   log likelihood =  589.87494  

ARCH family regression -- ARMA disturbances

Sample: 5888 - 6139                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =       0.54
Log likelihood =  589.8749                        Prob > chi2     =     0.7626

------------------------------------------------------------------------------
             |                 OPG
 hal_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
hal_returns  |
       _cons |  -.0015686   .0016381    -0.96   0.338    -.0047792     .001642
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .1229256     1.2229     0.10   0.920    -2.273915    2.519767
             |
          ma |
         L1. |  -.0716754   1.211104    -0.06   0.953    -2.445397    2.302046
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1129435   .0779795     1.45   0.148    -.0398935    .2657805
             |
       _cons |   .0004879   .0000457    10.67   0.000     .0003983    .0005775
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    29.8135
 Prob > chi2(40)           =     0.8804

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    29.0602
 Prob > chi2(40)           =     0.8998

.           
.           eststo: arch hal_returns, ar(1) ma(1) arch(1) garch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =   588.8194  
Iteration 1:   log likelihood =  590.24845  
Iteration 2:   log likelihood =  590.54164  
Iteration 3:   log likelihood =  591.17725  
Iteration 4:   log likelihood =  591.20138  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  591.22753  
BFGS stepping has contracted, resetting BFGS Hessian (0)
Iteration 6:   log likelihood =  591.25509  
Iteration 7:   log likelihood =  591.25585  (backed up)
Iteration 8:   log likelihood =  591.25592  (backed up)
Iteration 9:   log likelihood =  591.25612  (backed up)
Iteration 10:  log likelihood =  591.25612  (backed up)
Iteration 11:  log likelihood =  591.25615  
Iteration 12:  log likelihood =  591.25684  
BFGS stepping has contracted, resetting BFGS Hessian (1)
Iteration 13:  log likelihood =  591.25686  
Iteration 14:  log likelihood =  591.25686  (backed up)
(switching optimization to BHHH)
Iteration 15:  log likelihood =  591.25686  (backed up)
Iteration 16:  log likelihood =  591.25686  
Iteration 17:  log likelihood =  591.25687  

ARCH family regression -- ARMA disturbances

Sample: 5888 - 6139                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =       0.14
Log likelihood =  591.2569                        Prob > chi2     =     0.9304

------------------------------------------------------------------------------
             |                 OPG
 hal_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
hal_returns  |
       _cons |  -.0022089   .0015637    -1.41   0.158    -.0052737    .0008559
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0117164    2.82993     0.00   0.997    -5.534845    5.558278
             |
          ma |
         L1. |   .0138206   2.814054     0.00   0.996    -5.501623    5.529265
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0498355   .0360109     1.38   0.166    -.0207446    .1204157
             |
       garch |
         L1. |   .8633484   .1033359     8.35   0.000     .6608138    1.065883
             |
       _cons |   .0000486    .000045     1.08   0.279    -.0000395    .0001368
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    29.6669
 Prob > chi2(40)           =     0.8843

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    25.6170
 Prob > chi2(40)           =     0.9623

.           
.           eststo: arch hal_returns, ar(1) ma(1) arch(1) garch(1) het(sanc sanct)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  587.28645  
Iteration 1:   log likelihood =  589.57034  
Iteration 2:   log likelihood =  589.58724  (backed up)
Iteration 3:   log likelihood =  589.68114  
Iteration 4:   log likelihood =  590.02831  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  590.74166  (backed up)
Iteration 6:   log likelihood =   591.3013  
Iteration 7:   log likelihood =  591.30984  
Iteration 8:   log likelihood =  591.73694  
Iteration 9:   log likelihood =  591.73772  
Iteration 10:  log likelihood =  591.73828  
Iteration 11:  log likelihood =  591.73829  (backed up)
Iteration 12:  log likelihood =  591.74009  
Iteration 13:  log likelihood =  591.74118  
Iteration 14:  log likelihood =  591.75162  
(switching optimization to BHHH)
Iteration 15:  log likelihood =   591.7832  
Iteration 16:  log likelihood =  591.79386  
Iteration 17:  log likelihood =  591.85242  
Iteration 18:  log likelihood =  591.88739  
Iteration 19:  log likelihood =  591.95866  
(switching optimization to BFGS)
Iteration 20:  log likelihood =  592.06728  
Iteration 21:  log likelihood =  592.10416  
Iteration 22:  log likelihood =  592.11232  
Iteration 23:  log likelihood =  592.11713  
Iteration 24:  log likelihood =  592.11832  
Iteration 25:  log likelihood =  592.11839  
Iteration 26:  log likelihood =  592.11841  
Iteration 27:  log likelihood =  592.11841  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 5888 - 6139                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =     137.69
Log likelihood =  592.1184                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
 hal_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
hal_returns  |
       _cons |  -.0021532   .0015945    -1.35   0.177    -.0052784    .0009721
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.9683113   .0968668   -10.00   0.000    -1.158167   -.7784558
             |
          ma |
         L1. |   .9558193   .1094167     8.74   0.000     .7413664    1.170272
-------------+----------------------------------------------------------------
HET          |
        sanc |   .2578484   .2042764     1.26   0.207     -.142526    .6582228
       sanct |   .7095871   .5708256     1.24   0.214    -.4092106    1.828385
       _cons |  -9.391042   1.116483    -8.41   0.000    -11.57931   -7.202776
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |    .060924   .0537203     1.13   0.257    -.0443659    .1662138
             |
       garch |
         L1. |   .7685717   .2319923     3.31   0.001     .3138752    1.223268
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    29.7481
 Prob > chi2(40)           =     0.8821

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    28.0194
 Prob > chi2(40)           =     0.9231

.           
.           eststo: arch hal_returns, het(sanc sanct)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  585.58334  
Iteration 1:   log likelihood =  588.17335  
Iteration 2:   log likelihood =   588.8576  
Iteration 3:   log likelihood =  589.34314  
Iteration 4:   log likelihood =  589.53712  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  589.60789  
Iteration 6:   log likelihood =  589.62975  
Iteration 7:   log likelihood =  589.63008  
Iteration 8:   log likelihood =  589.63008  

Time-series regression -- multiplicative heteroskedasticity

Sample: 5888 - 6139                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  589.6301                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 hal_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
hal_returns  |
       _cons |  -.0018244   .0015383    -1.19   0.236    -.0048394    .0011906
-------------+----------------------------------------------------------------
HET          |
        sanc |   .2514017   .1560534     1.61   0.107    -.0544574    .5572608
       sanct |   .4443331   .3632255     1.22   0.221    -.2675758    1.156242
       _cons |  -7.617142   .1048095   -72.68   0.000    -7.822565   -7.411719
------------------------------------------------------------------------------
(est7 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    32.0380
 Prob > chi2(40)           =     0.8109

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.7663
 Prob > chi2(40)           =     0.4366

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sanc Sanctions sanct SanctionT
> hreats) nomtitles title(Halliburton 1992) nodep  

Halliburton 1992
-----------------------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)          (7)   
-----------------------------------------------------------------------------------------------------
hal_retu~s                                                                                           
Constant      -0.002       -0.002       -0.002       -0.002       -0.002       -0.002       -0.002   
             (0.002)      (0.002)      (0.002)      (0.002)      (0.002)      (0.002)      (0.002)   
-----------------------------------------------------------------------------------------------------
SIGMA2                                                                                               
Constant       0.001***     0.001***     0.001***                                                    
             (0.000)      (0.000)      (0.000)                                                       
-----------------------------------------------------------------------------------------------------
ARMA                                                                                                 
L.ar                        0.019                     0.123        0.012       -0.968***             
                          (0.057)                   (1.223)      (2.830)      (0.097)                
L.ma                                     0.019       -0.072        0.014        0.956***             
                                       (0.056)      (1.211)      (2.814)      (0.109)                
-----------------------------------------------------------------------------------------------------
ARCH                                                                                                 
L.arch                                                0.113        0.050        0.061                
                                                    (0.078)      (0.036)      (0.054)                
L.garch                                                            0.863***     0.769***             
                                                                 (0.103)      (0.232)                
Constant                                              0.000***     0.000                             
                                                    (0.000)      (0.000)                             
-----------------------------------------------------------------------------------------------------
HET                                                                                                  
Sanctions                                                                       0.258        0.251   
                                                                              (0.204)      (0.156)   
Sanction~s                                                                      0.710        0.444   
                                                                              (0.571)      (0.363)   
Constant                                                                       -9.391***    -7.617***
                                                                              (1.116)      (0.105)   
-----------------------------------------------------------------------------------------------------
N                252          252          252          252          252          252          252   
aic        -1172.892    -1170.987    -1170.986    -1169.750    -1170.514    -1168.237    -1171.260   
bic        -1165.833    -1160.399    -1160.397    -1152.103    -1149.337    -1140.001    -1157.142   
-----------------------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/halliburton1992.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Con
> stant sanc Sanctions sanct SanctionThreats) nomtitles title(Halliburton 1992) nodep replace
(note: file rawtables/halliburton1992.tex not found)
(output written to rawtables/halliburton1992.tex)

.   
.   * Colgate-Palmolive 1993 (Romania)
.   
.     * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen returns_cl = ln(cl_close/cl_close[_n-1])   
(3,148 missing values generated)

.           
.         * Sanctions Variable    
.           gen sanc = 1 if date > td("26jan1993")
(6,306 missing values generated)

.           recode sanc(.=0)       
(sanc: 6306 changes made)

.           
.         * Limit Time Period
.           keep if date > td("31jan1991") & date < td("01jan1994")
(12,356 observations deleted)

.           
.         * Set for analysis
.           tsset t
        time variable:  t, 5805 to 6542
                delta:  1 unit

.           
.         * Table A.21 Models
.           eststo clear

.           
.           eststo: arch returns_cl

(setting optimization to BHHH)
Iteration 0:   log likelihood =   2107.193  
Iteration 1:   log likelihood =   2107.193  

Time-series regression

Sample: 5805 - 6542                               Number of obs   =        738
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  2107.193                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
  returns_cl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |    .000783   .0005132     1.53   0.127     -.000223    .0017889
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0001938   8.17e-06    23.74   0.000     .0001778    .0002099
------------------------------------------------------------------------------
(est1 stored)

.                   
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    42.4312
 Prob > chi2(40)           =     0.3666

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    52.1748
 Prob > chi2(40)           =     0.0940

.           
.           eststo: arch returns_cl, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  2108.4465  
Iteration 1:   log likelihood =  2108.9446  
Iteration 2:   log likelihood =  2108.9512  
Iteration 3:   log likelihood =  2108.9516  
Iteration 4:   log likelihood =  2108.9516  

Time-series regression -- AR disturbances

Sample: 5805 - 6542                               Number of obs   =        738
Distribution: Gaussian                            Wald chi2(1)    =       4.10
Log likelihood =  2108.952                        Prob > chi2     =     0.0430

------------------------------------------------------------------------------
             |                 OPG
  returns_cl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_cl   |
       _cons |   .0007852   .0005527     1.42   0.155    -.0002981    .0018685
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0690104    .034096     2.02   0.043     .0021835    .1358373
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0001929   8.13e-06    23.74   0.000      .000177    .0002089
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.5185
 Prob > chi2(40)           =     0.5825

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    48.4311
 Prob > chi2(40)           =     0.1693

.           
.           eststo: arch returns_cl, ar(5)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  2110.0579  
Iteration 1:   log likelihood =  2110.4726  
Iteration 2:   log likelihood =  2110.4844  
Iteration 3:   log likelihood =  2110.4848  
Iteration 4:   log likelihood =  2110.4848  

Time-series regression -- AR disturbances

Sample: 5805 - 6542                               Number of obs   =        738
Distribution: Gaussian                            Wald chi2(1)    =       6.56
Log likelihood =  2110.485                        Prob > chi2     =     0.0104

------------------------------------------------------------------------------
             |                 OPG
  returns_cl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_cl   |
       _cons |   .0007769   .0004673     1.66   0.096     -.000139    .0016928
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L5. |  -.0943536   .0368299    -2.56   0.010    -.1665388   -.0221684
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0001921   8.15e-06    23.57   0.000     .0001762    .0002081
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    34.9391
 Prob > chi2(40)           =     0.6971

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    49.3494
 Prob > chi2(40)           =     0.1476

.           
.           eststo: arch returns_cl, ma(5)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  2110.1932  
Iteration 1:   log likelihood =  2110.6336  
Iteration 2:   log likelihood =  2110.6501  
Iteration 3:   log likelihood =  2110.6507  
Iteration 4:   log likelihood =  2110.6507  

Time-series regression -- MA disturbances

Sample: 5805 - 6542                               Number of obs   =        738
Distribution: Gaussian                            Wald chi2(1)    =       7.30
Log likelihood =  2110.651                        Prob > chi2     =     0.0069

------------------------------------------------------------------------------
             |                 OPG
  returns_cl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_cl   |
       _cons |   .0007761   .0004603     1.69   0.092    -.0001262    .0016784
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L5. |  -.0996616   .0368844    -2.70   0.007    -.1719538   -.0273694
-------------+----------------------------------------------------------------
     /SIGMA2 |    .000192   8.15e-06    23.56   0.000     .0001761     .000208
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    34.2781
 Prob > chi2(40)           =     0.7249

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    49.0662
 Prob > chi2(40)           =     0.1541

.           
.           eststo: arch returns_cl, ar(5) het(sanc)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  2100.8872  
Iteration 1:   log likelihood =   2109.385  
Iteration 2:   log likelihood =  2111.5308  
Iteration 3:   log likelihood =  2112.0938  
Iteration 4:   log likelihood =  2112.1492  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  2112.1572  
Iteration 6:   log likelihood =  2112.1584  
Iteration 7:   log likelihood =  2112.1584  
Iteration 8:   log likelihood =  2112.1584  

Time-series regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 5805 - 6542                               Number of obs   =        738
Distribution: Gaussian                            Wald chi2(1)    =       7.17
Log likelihood =  2112.158                        Prob > chi2     =     0.0074

------------------------------------------------------------------------------
             |                 OPG
  returns_cl |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_cl   |
       _cons |   .0007988    .000465     1.72   0.086    -.0001127    .0017103
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L5. |  -.0974505   .0363821    -2.68   0.007    -.1687582   -.0261429
-------------+----------------------------------------------------------------
HET          |
        sanc |   .2021707   .0882868     2.29   0.022     .0291318    .3752097
       _cons |  -8.626534   .0614182  -140.46   0.000    -8.746912   -8.506157
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    32.7074
 Prob > chi2(40)           =     0.7867

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.1360
 Prob > chi2(40)           =     0.4642

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sanc Sanctions) nomtitles titl
> e(Colgate-Palmolive 1993 (Romania)) nodep  

Colgate-Palmolive 1993 (Romania)
---------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)   
---------------------------------------------------------------------------
returns_cl                                                                 
Constant       0.001        0.001        0.001*       0.001*       0.001*  
             (0.001)      (0.001)      (0.000)      (0.000)      (0.000)   
---------------------------------------------------------------------------
SIGMA2                                                                     
Constant       0.000***     0.000***     0.000***     0.000***             
             (0.000)      (0.000)      (0.000)      (0.000)                
---------------------------------------------------------------------------
ARMA                                                                       
L.ar                        0.069**                                        
                          (0.034)                                          
L5.ar                                   -0.094**                  -0.097***
                                       (0.037)                   (0.036)   
L5.ma                                                -0.100***             
                                                    (0.037)                
---------------------------------------------------------------------------
HET                                                                        
Sanctions                                                          0.202** 
                                                                 (0.088)   
Constant                                                          -8.627***
                                                                 (0.061)   
---------------------------------------------------------------------------
N                738          738          738          738          738   
aic        -4210.386    -4211.903    -4214.970    -4215.301    -4216.317   
bic        -4201.178    -4198.091    -4201.158    -4201.490    -4197.901   
---------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/colgate1993.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constan
> t sanc Sanctions) nomtitles title(Colgate-Palmolive 1993 (Romania)) nodep replace
(note: file rawtables/colgate1993.tex not found)
(output written to rawtables/colgate1993.tex)

. 
.   * Kimberly-Clark 1993 (Romania)
.   
.     * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen returns_kmb = ln(kmb_close/kmb_close[_n-1])        
(3,148 missing values generated)

.           
.         * Sanctions Variable    
.           gen sanc = 1 if date > td("26jan1993")
(6,306 missing values generated)

.           recode sanc(.=0)       
(sanc: 6306 changes made)

.           
.         * Limit Time Period
.           keep if date > td("31jan1991") & date < td("01jan1994")
(12,356 observations deleted)

.           
.         * Set for analysis
.           tsset t
        time variable:  t, 5805 to 6542
                delta:  1 unit

.           
.         * Table A.22 Models       
.           eststo clear

.           
.           eststo: arch returns_kmb

(setting optimization to BHHH)
Iteration 0:   log likelihood =  2105.3333  
Iteration 1:   log likelihood =  2105.3333  

Time-series regression

Sample: 5805 - 6542                               Number of obs   =        738
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  2105.333                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 returns_kmb |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .0002841   .0005169     0.55   0.583     -.000729    .0012972
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0001948   7.20e-06    27.07   0.000     .0001807    .0002089
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    34.2864
 Prob > chi2(40)           =     0.7246

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    42.7908
 Prob > chi2(40)           =     0.3523

.           
.           eststo: arch returns_kmb, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  2105.5165  
Iteration 1:   log likelihood =  2105.6269  
Iteration 2:   log likelihood =    2105.63  
Iteration 3:   log likelihood =  2105.6304  
Iteration 4:   log likelihood =  2105.6305  

Time-series regression -- AR disturbances

Sample: 5805 - 6542                               Number of obs   =        738
Distribution: Gaussian                            Wald chi2(1)    =       0.90
Log likelihood =   2105.63                        Prob > chi2     =     0.3435

------------------------------------------------------------------------------
             |                 OPG
 returns_kmb |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_kmb  |
       _cons |   .0002833   .0005061     0.56   0.576    -.0007086    .0012752
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.0284062   .0299847    -0.95   0.343    -.0871751    .0303628
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0001947   7.19e-06    27.08   0.000     .0001806    .0002087
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    34.3567
 Prob > chi2(40)           =     0.7217

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    41.4045
 Prob > chi2(40)           =     0.4092

.           
.           eststo: arch returns_kmb, ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  2105.4731  
Iteration 1:   log likelihood =  2105.6511  
Iteration 2:   log likelihood =  2105.6609  
Iteration 3:   log likelihood =  2105.6618  
Iteration 4:   log likelihood =  2105.6619  
(switching optimization to BFGS)
Iteration 5:   log likelihood =   2105.662  

Time-series regression -- MA disturbances

Sample: 5805 - 6542                               Number of obs   =        738
Distribution: Gaussian                            Wald chi2(1)    =       1.09
Log likelihood =  2105.662                        Prob > chi2     =     0.2960

------------------------------------------------------------------------------
             |                 OPG
 returns_kmb |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_kmb  |
       _cons |    .000284   .0005043     0.56   0.573    -.0007044    .0012724
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |  -.0314303   .0300746    -1.05   0.296    -.0903755    .0275149
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0001946   7.19e-06    27.09   0.000     .0001806    .0002087
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    34.3880
 Prob > chi2(40)           =     0.7204

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    41.2594
 Prob > chi2(40)           =     0.4154

.           
.           eststo: arch returns_kmb, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  2106.1724  
Iteration 1:   log likelihood =  2107.0313  
Iteration 2:   log likelihood =  2107.4592  
Iteration 3:   log likelihood =  2107.4885  
Iteration 4:   log likelihood =  2107.4921  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  2107.4929  
Iteration 6:   log likelihood =  2107.4932  
Iteration 7:   log likelihood =  2107.4932  

Time-series regression -- ARMA disturbances

Sample: 5805 - 6542                               Number of obs   =        738
Distribution: Gaussian                            Wald chi2(2)    =     189.76
Log likelihood =  2107.493                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
 returns_kmb |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_kmb  |
       _cons |   .0002711   .0003751     0.72   0.470    -.0004641    .0010063
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .8573729   .1009512     8.49   0.000     .6595122    1.055234
             |
          ma |
         L1. |   -.896839    .087862   -10.21   0.000    -1.069045   -.7246328
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0001937   7.28e-06    26.60   0.000     .0001794     .000208
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    32.0438
 Prob > chi2(40)           =     0.8107

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    42.3676
 Prob > chi2(40)           =     0.3692

.           
.           eststo: arch returns_kmb, arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  2114.8579  
Iteration 1:   log likelihood =  2114.8693  
Iteration 2:   log likelihood =  2114.8705  
Iteration 3:   log likelihood =  2114.8707  
Iteration 4:   log likelihood =  2114.8707  

ARCH family regression

Sample: 5805 - 6542                               Number of obs   =        738
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  2114.871                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 returns_kmb |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_kmb  |
       _cons |   .0003275   .0004969     0.66   0.510    -.0006463    .0013014
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1346941   .0303961     4.43   0.000     .0751188    .1942693
             |
       _cons |   .0001679   8.44e-06    19.89   0.000     .0001514    .0001844
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    34.3605
 Prob > chi2(40)           =     0.7215

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    31.3015
 Prob > chi2(40)           =     0.8358

.           
.           eststo: arch returns_kmb, ar(1) ma(1) arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =   2114.687  
Iteration 1:   log likelihood =  2115.7223  
Iteration 2:   log likelihood =  2115.9736  
Iteration 3:   log likelihood =   2116.161  
Iteration 4:   log likelihood =  2116.1904  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  2116.2212  
Iteration 6:   log likelihood =  2116.2245  
Iteration 7:   log likelihood =   2116.225  
Iteration 8:   log likelihood =   2116.225  

ARCH family regression -- ARMA disturbances

Sample: 5805 - 6542                               Number of obs   =        738
Distribution: Gaussian                            Wald chi2(2)    =     180.47
Log likelihood =  2116.225                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
 returns_kmb |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_kmb  |
       _cons |    .000313   .0003753     0.83   0.404    -.0004226    .0010487
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .8873432   .1147276     7.73   0.000     .6624811    1.112205
             |
          ma |
         L1. |  -.9159509   .0995548    -9.20   0.000    -1.111075   -.7208272
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1292368   .0295834     4.37   0.000     .0712544    .1872192
             |
       _cons |   .0001681   8.47e-06    19.85   0.000     .0001515    .0001847
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    32.6029
 Prob > chi2(40)           =     0.7906

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    33.0096
 Prob > chi2(40)           =     0.7754

.           
.           eststo: arch returns_kmb, arch(1) het(sanc)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  2108.5564  
Iteration 1:   log likelihood =  2114.7474  
Iteration 2:   log likelihood =  2114.8803  
Iteration 3:   log likelihood =  2114.8866  
Iteration 4:   log likelihood =   2114.887  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  2114.8871  
Iteration 6:   log likelihood =  2114.8871  

ARCH family regression -- multiplicative heteroskedasticity

Sample: 5805 - 6542                               Number of obs   =        738
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  2114.887                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 returns_kmb |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_kmb  |
       _cons |   .0003234   .0004973     0.65   0.515    -.0006513     .001298
-------------+----------------------------------------------------------------
HET          |
        sanc |  -.0222022    .100307    -0.22   0.825    -.2188004     .174396
       _cons |  -8.684678   .0585614  -148.30   0.000    -8.799456     -8.5699
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1341625   .0306517     4.38   0.000     .0740862    .1942387
------------------------------------------------------------------------------
(est7 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    34.3535
 Prob > chi2(40)           =     0.7218

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    31.4227
 Prob > chi2(40)           =     0.8319

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sanc Sanctions) nomtitles titl
> e(Kimberly-Clark 1993 (Romania)) nodep  

Kimberly-Clark 1993 (Romania)
-----------------------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)          (7)   
-----------------------------------------------------------------------------------------------------
returns_~b                                                                                           
Constant       0.000        0.000        0.000        0.000        0.000        0.000        0.000   
             (0.001)      (0.001)      (0.001)      (0.000)      (0.000)      (0.000)      (0.000)   
-----------------------------------------------------------------------------------------------------
SIGMA2                                                                                               
Constant       0.000***     0.000***     0.000***     0.000***                                       
             (0.000)      (0.000)      (0.000)      (0.000)                                          
-----------------------------------------------------------------------------------------------------
ARMA                                                                                                 
L.ar                       -0.028                     0.857***                  0.887***             
                          (0.030)                   (0.101)                   (0.115)                
L.ma                                    -0.031       -0.897***                 -0.916***             
                                       (0.030)      (0.088)                   (0.100)                
-----------------------------------------------------------------------------------------------------
ARCH                                                                                                 
L.arch                                                             0.135***     0.129***     0.134***
                                                                 (0.030)      (0.030)      (0.031)   
Constant                                                           0.000***     0.000***             
                                                                 (0.000)      (0.000)                
-----------------------------------------------------------------------------------------------------
HET                                                                                                  
Sanctions                                                                                   -0.022   
                                                                                           (0.100)   
Constant                                                                                    -8.685***
                                                                                           (0.059)   
-----------------------------------------------------------------------------------------------------
N                738          738          738          738          738          738          738   
aic        -4206.667    -4205.261    -4205.324    -4206.986    -4223.741    -4222.450    -4221.774   
bic        -4197.459    -4191.449    -4191.512    -4188.571    -4209.930    -4199.430    -4203.358   
-----------------------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/kimberly1993.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Consta
> nt sanc Sanctions) nomtitles title(Kimberly-Clark 1993 (Romania)) nodep replace
(note: file rawtables/kimberly1993.tex not found)
(output written to rawtables/kimberly1993.tex)

.   
.   * Disney 1997 (France)
.   
.     * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen returns_dis = ln(dis_close/dis_close[_n-1])        
(1,489 missing values generated)

.           
.         * Sanctions Variable    
.           gen sanct = 1 if date > td("28sep1997") & date < td("18may1998")
(12,935 missing values generated)

.           recode sanct(.=0)
(sanct: 12935 changes made)

.           
.         * Limit Time Period
.           keep if date > td("31dec1996") & date < td("01jan1998")
(12,841 observations deleted)

.           
.         * Set for analysis
.           tsset t
        time variable:  t, 7301 to 7553
                delta:  1 unit

.           
.         * Table A.23 Models
.           eststo clear

.           
.           eststo: arch returns_dis

(setting optimization to BHHH)
Iteration 0:   log likelihood =  694.03012  
Iteration 1:   log likelihood =  694.03012  

Time-series regression

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  694.0301                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 returns_dis |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .0013842   .0009795     1.41   0.158    -.0005355    .0033039
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0002426   .0000183    13.22   0.000     .0002066    .0002785
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    49.2882
 Prob > chi2(40)           =     0.1490

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    52.7325
 Prob > chi2(40)           =     0.0856

.           
.           eststo: arch returns_dis, ar(2)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  695.54194  
Iteration 1:   log likelihood =  696.45463  
Iteration 2:   log likelihood =  696.46427  
Iteration 3:   log likelihood =   696.4645  
Iteration 4:   log likelihood =  696.46451  

Time-series regression -- AR disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       4.38
Log likelihood =  696.4645                        Prob > chi2     =     0.0363

------------------------------------------------------------------------------
             |                 OPG
 returns_dis |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_dis  |
       _cons |     .00138   .0008557     1.61   0.107    -.0002971    .0030572
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L2. |  -.1384412    .066122    -2.09   0.036     -.268038   -.0088445
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0002379   .0000177    13.42   0.000     .0002032    .0002727
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    38.1235
 Prob > chi2(40)           =     0.5550

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    55.3689
 Prob > chi2(40)           =     0.0537

.           
.           eststo: arch returns_dis, ar(2) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =   696.9225  
Iteration 1:   log likelihood =  697.68221  
Iteration 2:   log likelihood =  697.71908  
Iteration 3:   log likelihood =  697.72275  
Iteration 4:   log likelihood =  697.72307  
(switching optimization to BFGS)
Iteration 5:   log likelihood =   697.7231  

Time-series regression -- ARMA disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =       9.01
Log likelihood =  697.7231                        Prob > chi2     =     0.0111

------------------------------------------------------------------------------
             |                 OPG
 returns_dis |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_dis  |
       _cons |   .0013893    .000793     1.75   0.080     -.000165    .0029436
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L2. |  -.1427168   .0666842    -2.14   0.032    -.2734153   -.0120182
             |
          ma |
         L1. |  -.1007428   .0553621    -1.82   0.069    -.2092504    .0077649
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0002356   .0000183    12.87   0.000     .0001997    .0002714
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    35.9031
 Prob > chi2(40)           =     0.6552

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    52.0214
 Prob > chi2(40)           =     0.0965

.           
.           eststo: arch returns_dis, ar(2) ma(1) arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  695.21388  
Iteration 1:   log likelihood =  697.35241  
Iteration 2:   log likelihood =  698.89908  
Iteration 3:   log likelihood =  698.94367  
Iteration 4:   log likelihood =  698.94747  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  698.94777  
Iteration 6:   log likelihood =   698.9478  

ARCH family regression -- ARMA disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =       5.66
Log likelihood =  698.9478                        Prob > chi2     =     0.0591

------------------------------------------------------------------------------
             |                 OPG
 returns_dis |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_dis  |
       _cons |   .0013591   .0008157     1.67   0.096    -.0002397    .0029579
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L2. |   -.136588   .0646625    -2.11   0.035    -.2633242   -.0098517
             |
          ma |
         L1. |   -.071622   .0730832    -0.98   0.327    -.2148624    .0716184
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0595706   .0498178     1.20   0.232    -.0380705    .1572117
             |
       _cons |   .0002201    .000019    11.59   0.000     .0001829    .0002574
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    36.6915
 Prob > chi2(40)           =     0.6200

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    47.4401
 Prob > chi2(40)           =     0.1952

.           
.           eststo: arch returns_dis, ar(2) ma(1) arch(1) garch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  695.03507  
Iteration 1:   log likelihood =  698.82041  
Iteration 2:   log likelihood =  698.93855  
Iteration 3:   log likelihood =  698.94744  
Iteration 4:   log likelihood =  698.94791  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  698.94799  
Iteration 6:   log likelihood =    698.948  

ARCH family regression -- ARMA disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =       5.57
Log likelihood =   698.948                        Prob > chi2     =     0.0619

------------------------------------------------------------------------------
             |                 OPG
 returns_dis |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_dis  |
       _cons |   .0013579   .0008353     1.63   0.104    -.0002792     .002995
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L2. |  -.1363906    .065083    -2.10   0.036    -.2639509   -.0088303
             |
          ma |
         L1. |  -.0713882   .0730791    -0.98   0.329    -.2146207    .0718442
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0597786   .0499288     1.20   0.231      -.03808    .1576372
             |
       garch |
         L1. |   .0222087   1.121199     0.02   0.984    -2.175302    2.219719
             |
       _cons |   .0002148   .0002626     0.82   0.413    -.0002999    .0007295
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    36.6883
 Prob > chi2(40)           =     0.6201

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    47.3898
 Prob > chi2(40)           =     0.1966

.           
.           eststo: arch returns_dis, ar(2) ma(1) arch(1) het(sanct)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  700.96845  
Iteration 1:   log likelihood =  702.60101  
Iteration 2:   log likelihood =   703.3604  
Iteration 3:   log likelihood =   703.4341  
Iteration 4:   log likelihood =  703.44422  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  703.44561  
Iteration 6:   log likelihood =  703.44606  
Iteration 7:   log likelihood =  703.44607  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =       5.63
Log likelihood =  703.4461                        Prob > chi2     =     0.0600

------------------------------------------------------------------------------
             |                 OPG
 returns_dis |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_dis  |
       _cons |   .0011599   .0007911     1.47   0.143    -.0003907    .0027105
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L2. |  -.1387443   .0693723    -2.00   0.046    -.2747115   -.0027771
             |
          ma |
         L1. |  -.0720287   .0710677    -1.01   0.311    -.2113188    .0672614
-------------+----------------------------------------------------------------
HET          |
       sanct |   .6140376   .1947928     3.15   0.002     .2322508    .9958244
       _cons |  -8.613866    .132052   -65.23   0.000    -8.872683   -8.355049
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0557018   .0662888     0.84   0.401    -.0742219    .1856256
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    35.6145
 Prob > chi2(40)           =     0.6679

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    33.5561
 Prob > chi2(40)           =     0.7541

.           
.           eststo: arch returns_dis, ar(2) ma(1) het(sanct)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  700.99853  
Iteration 1:   log likelihood =  702.63435  
Iteration 2:   log likelihood =  702.75749  
Iteration 3:   log likelihood =  702.78123  
Iteration 4:   log likelihood =  702.78566  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  702.78645  
Iteration 6:   log likelihood =  702.78664  
Iteration 7:   log likelihood =  702.78664  

Time-series regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =       7.22
Log likelihood =  702.7866                        Prob > chi2     =     0.0270

------------------------------------------------------------------------------
             |                 OPG
 returns_dis |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_dis  |
       _cons |   .0011245   .0007834     1.44   0.151     -.000411    .0026599
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L2. |  -.1454858   .0705251    -2.06   0.039    -.2837126   -.0072591
             |
          ma |
         L1. |  -.0837058   .0609764    -1.37   0.170    -.2032173    .0358056
-------------+----------------------------------------------------------------
HET          |
       sanct |   .6224867   .1880554     3.31   0.001      .253905    .9910684
       _cons |  -8.555898   .1168931   -73.19   0.000    -8.785004   -8.326791
------------------------------------------------------------------------------
(est7 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    34.6602
 Prob > chi2(40)           =     0.7090

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.2371
 Prob > chi2(40)           =     0.5953

.           
.           eststo: arch returns_dis, ar(2) het(sanct)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  699.88912  
Iteration 1:   log likelihood =  701.66102  
Iteration 2:   log likelihood =  701.87966  
Iteration 3:   log likelihood =  701.92123  
Iteration 4:   log likelihood =  701.92992  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  701.93152  
Iteration 6:   log likelihood =  701.93192  
Iteration 7:   log likelihood =  701.93192  

Time-series regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       4.07
Log likelihood =  701.9319                        Prob > chi2     =     0.0436

------------------------------------------------------------------------------
             |                 OPG
 returns_dis |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_dis  |
       _cons |   .0011075   .0008251     1.34   0.180    -.0005096    .0027246
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L2. |   -.140685   .0697311    -2.02   0.044    -.2773554   -.0040145
-------------+----------------------------------------------------------------
HET          |
       sanct |   .6430527   .1836804     3.50   0.000     .2830458     1.00306
       _cons |  -8.554501   .1160544   -73.71   0.000    -8.781964   -8.327039
------------------------------------------------------------------------------
(est8 stored)

.         
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    35.2972
 Prob > chi2(40)           =     0.6817

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    38.1256
 Prob > chi2(40)           =     0.5549

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sanc Sanctions) nomtitles titl
> e(Disney 1997 (France)) nodep  

Disney 1997 (France)
------------------------------------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)          (7)          (8)   
------------------------------------------------------------------------------------------------------------------
returns_~s                                                                                                        
Constant       0.001        0.001        0.001*       0.001*       0.001        0.001        0.001        0.001   
             (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)   
------------------------------------------------------------------------------------------------------------------
SIGMA2                                                                                                            
Constant       0.000***     0.000***     0.000***                                                                 
             (0.000)      (0.000)      (0.000)                                                                    
------------------------------------------------------------------------------------------------------------------
ARMA                                                                                                              
L2.ar                      -0.138**     -0.143**     -0.137**     -0.136**     -0.139**     -0.145**     -0.141** 
                          (0.066)      (0.067)      (0.065)      (0.065)      (0.069)      (0.071)      (0.070)   
L.ma                                    -0.101*      -0.072       -0.071       -0.072       -0.084                
                                       (0.055)      (0.073)      (0.073)      (0.071)      (0.061)                
------------------------------------------------------------------------------------------------------------------
ARCH                                                                                                              
L.arch                                                0.060        0.060        0.056                             
                                                    (0.050)      (0.050)      (0.066)                             
L.garch                                                            0.022                                          
                                                                 (1.121)                                          
Constant                                              0.000***     0.000                                          
                                                    (0.000)      (0.000)                                          
------------------------------------------------------------------------------------------------------------------
HET                                                                                                               
sanct                                                                           0.614***     0.622***     0.643***
                                                                              (0.195)      (0.188)      (0.184)   
Constant                                                                       -8.614***    -8.556***    -8.555***
                                                                              (0.132)      (0.117)      (0.116)   
------------------------------------------------------------------------------------------------------------------
N                253          253          253          253          253          253          253          253   
aic        -1384.060    -1386.929    -1387.446    -1387.896    -1385.896    -1394.892    -1395.573    -1395.864   
bic        -1376.993    -1376.329    -1373.313    -1370.229    -1364.696    -1373.692    -1377.906    -1381.730   
------------------------------------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/disney1997.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant
>  sanc Sanctions) nomtitles title(Disney 1997 (France)) nodep replace
(note: file rawtables/disney1997.tex not found)
(output written to rawtables/disney1997.tex)

. 
.   * AT&T Corp (France)
.   
.     * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen returns_t = ln(t_close/t_close[_n-1])      
(3,987 missing values generated)

.           
.         * Sanctions Variable    
.           gen sanct = 1 if date > td("28sep1997") & date < td("18may1998")
(12,935 missing values generated)

.           recode sanct(.=0)
(sanct: 12935 changes made)

.           
.         * Limit Time Period
.           keep if date > td("31dec1996") & date < td("01jan1998")
(12,841 observations deleted)

.           
.         * Set for analysis
.           tsset t
        time variable:  t, 7301 to 7553
                delta:  1 unit

.           
.         * Table A.24 Models
.           eststo clear

.           
.           eststo: arch returns_t

(setting optimization to BHHH)
Iteration 0:   log likelihood =  678.63795  
Iteration 1:   log likelihood =  678.63795  

Time-series regression

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =   678.638                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
   returns_t |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .0013638   .0010475     1.30   0.193    -.0006893    .0034169
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0002739   .0000236    11.61   0.000     .0002277    .0003202
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    50.5321
 Prob > chi2(40)           =     0.1229

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    39.8702
 Prob > chi2(40)           =     0.4760

.           
.           eststo: arch returns_t, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  679.52347  
Iteration 1:   log likelihood =  680.36567  
Iteration 2:   log likelihood =  680.37664  
Iteration 3:   log likelihood =  680.37688  
Iteration 4:   log likelihood =  680.37689  

Time-series regression -- AR disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       3.68
Log likelihood =  680.3769                        Prob > chi2     =     0.0552

------------------------------------------------------------------------------
             |                 OPG
   returns_t |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_t    |
       _cons |   .0013639   .0009349     1.46   0.145    -.0004684    .0031963
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.1169388   .0609799    -1.92   0.055    -.2364572    .0025797
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0002702   .0000231    11.68   0.000     .0002249    .0003155
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    48.0209
 Prob > chi2(40)           =     0.1797

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    42.5591
 Prob > chi2(40)           =     0.3615

.           
.           eststo: arch returns_t, ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  679.97688  
Iteration 1:   log likelihood =  680.77382  
Iteration 2:   log likelihood =  680.78634  
Iteration 3:   log likelihood =  680.78678  
Iteration 4:   log likelihood =  680.78681  

Time-series regression -- MA disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       5.99
Log likelihood =  680.7868                        Prob > chi2     =     0.0144

------------------------------------------------------------------------------
             |                 OPG
   returns_t |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_t    |
       _cons |   .0013644   .0008915     1.53   0.126     -.000383    .0031117
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |  -.1471337   .0601301    -2.45   0.014    -.2649866   -.0292808
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0002693    .000023    11.73   0.000     .0002243    .0003143
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    46.6008
 Prob > chi2(40)           =     0.2193

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    43.2601
 Prob > chi2(40)           =     0.3339

.           
.           eststo: arch returns_t, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  679.94889  
Iteration 1:   log likelihood =  681.48806  
Iteration 2:   log likelihood =  682.04743  
Iteration 3:   log likelihood =   682.0646  
Iteration 4:   log likelihood =  682.06476  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  682.06477  
Iteration 6:   log likelihood =  682.06479  

Time-series regression -- ARMA disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =      17.00
Log likelihood =  682.0648                        Prob > chi2     =     0.0002

------------------------------------------------------------------------------
             |                 OPG
   returns_t |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_t    |
       _cons |   .0013675    .000768     1.78   0.075    -.0001378    .0028728
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .4338812   .2796519     1.55   0.121    -.1142264    .9819888
             |
          ma |
         L1. |  -.5832722   .2558381    -2.28   0.023    -1.084706   -.0818387
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0002666   .0000227    11.76   0.000     .0002222    .0003111
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    41.3112
 Prob > chi2(40)           =     0.4131

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    42.2278
 Prob > chi2(40)           =     0.3749

.           
.           eststo: arch returns_t, ar(3)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  680.32357  
Iteration 1:   log likelihood =  681.05972  
Iteration 2:   log likelihood =  681.09121  
Iteration 3:   log likelihood =   681.0928  
Iteration 4:   log likelihood =  681.09288  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  681.09289  

Time-series regression -- AR disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       5.36
Log likelihood =  681.0929                        Prob > chi2     =     0.0206

------------------------------------------------------------------------------
             |                 OPG
   returns_t |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_t    |
       _cons |   .0013547   .0009112     1.49   0.137    -.0004312    .0031405
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L3. |  -.1390446   .0600556    -2.32   0.021    -.2567513   -.0213379
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0002687   .0000246    10.90   0.000     .0002204     .000317
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.8494
 Prob > chi2(40)           =     0.4330

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.8450
 Prob > chi2(40)           =     0.5677

.           
.           eststo: arch returns_t, ar(3) arch(1) 

(setting optimization to BHHH)
Iteration 0:   log likelihood =  680.90669  
Iteration 1:   log likelihood =  681.61876  
Iteration 2:   log likelihood =  681.64862  
Iteration 3:   log likelihood =  681.65271  
Iteration 4:   log likelihood =  681.65373  
(switching optimization to BFGS)
Iteration 5:   log likelihood =   681.6543  
Iteration 6:   log likelihood =   681.6549  
Iteration 7:   log likelihood =   681.6549  

ARCH family regression -- AR disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       4.78
Log likelihood =  681.6549                        Prob > chi2     =     0.0289

------------------------------------------------------------------------------
             |                 OPG
   returns_t |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_t    |
       _cons |   .0015239   .0009038     1.69   0.092    -.0002475    .0032953
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L3. |  -.1339871    .061307    -2.19   0.029    -.2541465   -.0138277
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0754838   .0949159     0.80   0.426    -.1105479    .2615155
             |
       _cons |   .0002485   .0000292     8.52   0.000     .0001913    .0003057
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.9846
 Prob > chi2(40)           =     0.4271

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    35.3967
 Prob > chi2(40)           =     0.6774

.           
.           eststo: arch returns_t, ar(3) arch(1) garch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  680.74154  
Iteration 1:   log likelihood =  682.28997  
Iteration 2:   log likelihood =  682.35277  
Iteration 3:   log likelihood =  682.68969  
Iteration 4:   log likelihood =  682.75384  
(switching optimization to BFGS)
Iteration 5:   log likelihood =   682.7762  
Iteration 6:   log likelihood =  682.83921  
Iteration 7:   log likelihood =  682.84326  
Iteration 8:   log likelihood =  682.84347  
Iteration 9:   log likelihood =  682.84348  
Iteration 10:  log likelihood =  682.84348  

ARCH family regression -- AR disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       3.97
Log likelihood =  682.8435                        Prob > chi2     =     0.0464

------------------------------------------------------------------------------
             |                 OPG
   returns_t |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_t    |
       _cons |   .0015612   .0009071     1.72   0.085    -.0002166     .003339
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L3. |  -.1366564    .068602    -1.99   0.046    -.2711138    -.002199
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0733466   .0723247     1.01   0.311    -.0684073    .2151004
             |
       garch |
         L1. |   .7828181   .2266708     3.45   0.001     .3385514    1.227085
             |
       _cons |   .0000392   .0000461     0.85   0.396    -.0000512    .0001296
------------------------------------------------------------------------------
(est7 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.0587
 Prob > chi2(40)           =     0.4677

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    32.1949
 Prob > chi2(40)           =     0.8054

.           
.           eststo: arch returns_t, ar(3) het(sanct)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  677.76261  
Iteration 1:   log likelihood =  681.09594  
Iteration 2:   log likelihood =  681.23292  
Iteration 3:   log likelihood =  681.24354  
Iteration 4:   log likelihood =  681.24417  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  681.24421  

Time-series regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       5.21
Log likelihood =  681.2442                        Prob > chi2     =     0.0224

------------------------------------------------------------------------------
             |                 OPG
   returns_t |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_t    |
       _cons |   .0013024   .0009163     1.42   0.155    -.0004935    .0030982
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L3. |  -.1367712   .0598964    -2.28   0.022    -.2541661   -.0193763
-------------+----------------------------------------------------------------
HET          |
       sanct |   .1114231    .212297     0.52   0.600    -.3046714    .5275176
       _cons |  -8.252279   .1030125   -80.11   0.000     -8.45418   -8.050378
------------------------------------------------------------------------------
(est8 stored)

. 
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.7602
 Prob > chi2(40)           =     0.4368

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.7411
 Prob > chi2(40)           =     0.5724

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sanct SanctionsThreat) nomtitl
> es title(ATT Corp 1997 (France)) nodep  

ATT Corp 1997 (France)
------------------------------------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)          (7)          (8)   
------------------------------------------------------------------------------------------------------------------
returns_t                                                                                                         
Constant       0.001        0.001        0.001        0.001*       0.001        0.002*       0.002*       0.001   
             (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)   
------------------------------------------------------------------------------------------------------------------
SIGMA2                                                                                                            
Constant       0.000***     0.000***     0.000***     0.000***     0.000***                                       
             (0.000)      (0.000)      (0.000)      (0.000)      (0.000)                                          
------------------------------------------------------------------------------------------------------------------
ARMA                                                                                                              
L.ar                       -0.117*                    0.434                                                       
                          (0.061)                   (0.280)                                                       
L3.ar                                                             -0.139**     -0.134**     -0.137**     -0.137** 
                                                                 (0.060)      (0.061)      (0.069)      (0.060)   
L.ma                                    -0.147**     -0.583**                                                     
                                       (0.060)      (0.256)                                                       
------------------------------------------------------------------------------------------------------------------
ARCH                                                                                                              
L.arch                                                                          0.075        0.073                
                                                                              (0.095)      (0.072)                
L.garch                                                                                      0.783***             
                                                                                           (0.227)                
Constant                                                                        0.000***     0.000                
                                                                              (0.000)      (0.000)                
------------------------------------------------------------------------------------------------------------------
HET                                                                                                               
Sanction~t                                                                                                0.111   
                                                                                                        (0.212)   
Constant                                                                                                 -8.252***
                                                                                                        (0.103)   
------------------------------------------------------------------------------------------------------------------
N                253          253          253          253          253          253          253          253   
aic        -1353.276    -1354.754    -1355.574    -1356.130    -1356.186    -1355.310    -1355.687    -1354.488   
bic        -1346.209    -1344.154    -1344.973    -1341.996    -1345.586    -1341.176    -1338.020    -1340.355   
------------------------------------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/atandt1997.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant
>  sanct SanctionsThreat) nomtitles title(ATT Corp 1997 (France)) nodep replace
(note: file rawtables/atandt1997.tex not found)
(output written to rawtables/atandt1997.tex)

.           
.   * Ford 1991 (Germany)
.   
.     * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen returns_f = ln(f_close/f_close[_n-1])      
(3,146 missing values generated)

.           
.         * Sanctions Variable    
.           gen sanct = 1 if date > td("31jan1991") & date < td("03mar1991")
(13,074 missing values generated)

.           recode sanct(.=0)  
(sanct: 13074 changes made)

.           
.         * Limit Time Period
.           keep if date > td("31dec1990") & date < td("01jan1992")
(12,841 observations deleted)

.           
.         * Set for analysis
.           tsset t
        time variable:  t, 5783 to 6035
                delta:  1 unit

.           
.         * Table A.25 Models
.           eststo clear

.           
.           eststo: arch returns_f

(setting optimization to BHHH)
Iteration 0:   log likelihood =  627.97769  
Iteration 1:   log likelihood =  627.97769  

Time-series regression

Sample: 5783 - 6035                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  627.9777                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
   returns_f |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .0002166   .0013947     0.16   0.877    -.0025169    .0029501
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0004089   .0000281    14.55   0.000     .0003538     .000464
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    34.6967
 Prob > chi2(40)           =     0.7074

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    13.3851
 Prob > chi2(40)           =     1.0000

.           
.           eststo: arch returns_f, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  625.78989  
Iteration 1:   log likelihood =  627.35405  
Iteration 2:   log likelihood =  627.80479  
Iteration 3:   log likelihood =  627.97351  
Iteration 4:   log likelihood =  628.00411  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  628.01451  
Iteration 6:   log likelihood =   628.0192  
Iteration 7:   log likelihood =  628.01925  

Time-series regression -- AR disturbances

Sample: 5783 - 6035                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       0.10
Log likelihood =  628.0193                        Prob > chi2     =     0.7487

------------------------------------------------------------------------------
             |                 OPG
   returns_f |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_f    |
       _cons |   .0002179   .0013799     0.16   0.875    -.0024866    .0029224
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.0181299    .056597    -0.32   0.749    -.1290579    .0927982
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0004087   .0000291    14.06   0.000     .0003518    .0004657
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    34.3580
 Prob > chi2(40)           =     0.7216

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    13.2313
 Prob > chi2(40)           =     1.0000

.           
.           eststo: arch returns_f, ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  625.78248  
Iteration 1:   log likelihood =  627.41168  
Iteration 2:   log likelihood =  627.82396  
Iteration 3:   log likelihood =  627.95717  
Iteration 4:   log likelihood =  628.00211  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  628.01679  
Iteration 6:   log likelihood =  628.02331  
Iteration 7:   log likelihood =  628.02333  

Time-series regression -- MA disturbances

Sample: 5783 - 6035                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       0.12
Log likelihood =  628.0233                        Prob > chi2     =     0.7262

------------------------------------------------------------------------------
             |                 OPG
   returns_f |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_f    |
       _cons |    .000218   .0013776     0.16   0.874    -.0024821    .0029181
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |  -.0199024    .056832    -0.35   0.726     -.131291    .0914862
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0004087    .000029    14.09   0.000     .0003519    .0004656
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    34.3232
 Prob > chi2(40)           =     0.7231

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    13.2272
 Prob > chi2(40)           =     1.0000

.           
.           eststo: arch returns_f, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  625.76079  
Iteration 1:   log likelihood =  626.32971  
Iteration 2:   log likelihood =  626.81339  
Iteration 3:   log likelihood =  627.72693  
Iteration 4:   log likelihood =  627.75782  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  627.95616  
BFGS stepping has contracted, resetting BFGS Hessian (0)
Iteration 6:   log likelihood =   628.0455  
Iteration 7:   log likelihood =  628.05131  (backed up)
Iteration 8:   log likelihood =  628.06614  (backed up)
Iteration 9:   log likelihood =   628.0662  (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (1)
Iteration 10:  log likelihood =  628.08267  
Iteration 11:  log likelihood =  628.08284  (backed up)
Iteration 12:  log likelihood =    628.083  (backed up)
Iteration 13:  log likelihood =   628.0838  (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (2)
Iteration 14:  log likelihood =  628.08438  
(switching optimization to BHHH)
Iteration 15:  log likelihood =  628.08438  (backed up)
Iteration 16:  log likelihood =  628.08439  

Time-series regression -- ARMA disturbances

Sample: 5783 - 6035                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =       0.37
Log likelihood =  628.0844                        Prob > chi2     =     0.8292

------------------------------------------------------------------------------
             |                 OPG
   returns_f |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_f    |
       _cons |   .0002201   .0013509     0.16   0.871    -.0024276    .0028677
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .3616241   1.911085     0.19   0.850    -3.384034    4.107282
             |
          ma |
         L1. |  -.3887345   1.889918    -0.21   0.837    -4.092906    3.315437
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0004085   .0000295    13.85   0.000     .0003507    .0004663
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    34.0343
 Prob > chi2(40)           =     0.7349

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    13.3877
 Prob > chi2(40)           =     1.0000

.           
.           eststo: arch returns_f, het(sanct)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  624.08922  
Iteration 1:   log likelihood =  628.17743  
Iteration 2:   log likelihood =  631.03944  
Iteration 3:   log likelihood =  631.25262  
Iteration 4:   log likelihood =  631.63839  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  631.65372  
Iteration 6:   log likelihood =   631.6587  
Iteration 7:   log likelihood =  631.65886  
Iteration 8:   log likelihood =  631.65886  

Time-series regression -- multiplicative heteroskedasticity

Sample: 5783 - 6035                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  631.6589                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
   returns_f |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_f    |
       _cons |  -.0001655   .0013529    -0.12   0.903     -.002817    .0024861
-------------+----------------------------------------------------------------
HET          |
       sanct |   .8027971   .3524289     2.28   0.023      .112049    1.493545
       _cons |   -7.89469   .0668646  -118.07   0.000    -8.025742   -7.763638
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    32.0982
 Prob > chi2(40)           =     0.8088

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    12.9509
 Prob > chi2(40)           =     1.0000

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sanct SanctionsThreat) nomtitl
> es title(Ford 1991 (Germany)) nodep  

Ford 1991 (Germany)
---------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)   
---------------------------------------------------------------------------
returns_f                                                                  
Constant       0.000        0.000        0.000        0.000       -0.000   
             (0.001)      (0.001)      (0.001)      (0.001)      (0.001)   
---------------------------------------------------------------------------
SIGMA2                                                                     
Constant       0.000***     0.000***     0.000***     0.000***             
             (0.000)      (0.000)      (0.000)      (0.000)                
---------------------------------------------------------------------------
ARMA                                                                       
L.ar                       -0.018                     0.362                
                          (0.057)                   (1.911)                
L.ma                                    -0.020       -0.389                
                                       (0.057)      (1.890)                
---------------------------------------------------------------------------
HET                                                                        
Sanction~t                                                         0.803** 
                                                                 (0.352)   
Constant                                                          -7.895***
                                                                 (0.067)   
---------------------------------------------------------------------------
N                253          253          253          253          253   
aic        -1251.955    -1250.039    -1250.047    -1248.169    -1257.318   
bic        -1244.889    -1239.438    -1239.446    -1234.035    -1246.718   
---------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/ford1991.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant s
> anct SanctionsThreat) nomtitles title(Ford 1991 (Germany)) nodep replace
(note: file rawtables/ford1991.tex not found)
(output written to rawtables/ford1991.tex)

.          
.   * TJX Companies 1991 (Germany)
.   
.     * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen returns_tjx = ln(tjx_close/tjx_close[_n-1])
(3,148 missing values generated)

. 
.         * Sanctions Variable    
.           gen sanct = 1 if date > td("31jan1991") & date < td("03mar1991")
(13,074 missing values generated)

.           recode sanct(.=0)  
(sanct: 13074 changes made)

.           
.         * Limit Time Period
.           keep if date > td("31dec1990") & date < td("01jan1992")
(12,841 observations deleted)

.           
.         * Set for analysis
.           tsset t
        time variable:  t, 5783 to 6035
                delta:  1 unit

.           
.         * Table A.26 Models
.           eststo clear

.           
.           eststo: arch returns_tjx

(setting optimization to BHHH)
Iteration 0:   log likelihood =  539.67683  
Iteration 1:   log likelihood =  539.67683  

Time-series regression

Sample: 5783 - 6035                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  539.6768                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 returns_tjx |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .0017368   .0018253     0.95   0.341    -.0018408    .0053144
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0008217   .0000613    13.41   0.000     .0007016    .0009419
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    27.9668
 Prob > chi2(40)           =     0.9242

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.4468
 Prob > chi2(40)           =     0.5858

.           
.           eststo: arch returns_tjx, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  536.36931  
Iteration 1:   log likelihood =  539.46297  
Iteration 2:   log likelihood =  539.71446  
Iteration 3:   log likelihood =  539.74338  
Iteration 4:   log likelihood =  539.74676  
(switching optimization to BFGS)
Iteration 5:   log likelihood =   539.7471  
Iteration 6:   log likelihood =  539.74714  

Time-series regression -- AR disturbances

Sample: 5783 - 6035                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       0.12
Log likelihood =  539.7471                        Prob > chi2     =     0.7238

------------------------------------------------------------------------------
             |                 OPG
 returns_tjx |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_tjx  |
       _cons |     .00173   .0017979     0.96   0.336    -.0017937    .0052537
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.0236277   .0668491    -0.35   0.724    -.1546496    .1073942
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0008212   .0000622    13.21   0.000     .0006994    .0009431
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    28.3191
 Prob > chi2(40)           =     0.9168

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    38.1205
 Prob > chi2(40)           =     0.5551

.           
.           eststo: arch returns_tjx, ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  536.02261  
Iteration 1:   log likelihood =  539.33412  
Iteration 2:   log likelihood =  539.72271  
Iteration 3:   log likelihood =  539.75476  
Iteration 4:   log likelihood =  539.75759  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  539.75785  
Iteration 6:   log likelihood =  539.75788  

Time-series regression -- MA disturbances

Sample: 5783 - 6035                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       0.17
Log likelihood =  539.7579                        Prob > chi2     =     0.6822

------------------------------------------------------------------------------
             |                 OPG
 returns_tjx |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_tjx  |
       _cons |   .0017294   .0017914     0.97   0.334    -.0017817    .0052405
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |  -.0273769   .0668703    -0.41   0.682    -.1584402    .1036865
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0008212   .0000623    13.18   0.000      .000699    .0009433
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    28.3821
 Prob > chi2(40)           =     0.9154

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    38.2148
 Prob > chi2(40)           =     0.5508

.           
.           eststo: arch returns_tjx, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  536.67382  
Iteration 1:   log likelihood =  538.97991  
Iteration 2:   log likelihood =  540.20619  
Iteration 3:   log likelihood =  540.61836  
Iteration 4:   log likelihood =  540.65635  
(switching optimization to BFGS)
Iteration 5:   log likelihood =   540.8604  
Iteration 6:   log likelihood =  541.08061  
Iteration 7:   log likelihood =  541.10136  
Iteration 8:   log likelihood =  541.10243  
Iteration 9:   log likelihood =  541.10251  
Iteration 10:  log likelihood =  541.10252  

Time-series regression -- ARMA disturbances

Sample: 5783 - 6035                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =      16.36
Log likelihood =  541.1025                        Prob > chi2     =     0.0003

------------------------------------------------------------------------------
             |                 OPG
 returns_tjx |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_tjx  |
       _cons |   .0017164   .0014532     1.18   0.238    -.0011319    .0045646
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .6238736   .3545442     1.76   0.078    -.0710202    1.318768
             |
          ma |
         L1. |  -.7076066   .3163623    -2.24   0.025    -1.327665   -.0875478
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0008125   .0000651    12.47   0.000     .0006849    .0009402
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    26.1056
 Prob > chi2(40)           =     0.9559

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    38.8672
 Prob > chi2(40)           =     0.5212

.           
.           eststo: arch returns_tjx, ma(3)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  539.02613  
Iteration 1:   log likelihood =  541.74434  
Iteration 2:   log likelihood =  541.90022  
Iteration 3:   log likelihood =  541.91396  
Iteration 4:   log likelihood =   541.9146  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  541.91463  

Time-series regression -- MA disturbances

Sample: 5783 - 6035                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       4.85
Log likelihood =  541.9146                        Prob > chi2     =     0.0276

------------------------------------------------------------------------------
             |                 OPG
 returns_tjx |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_tjx  |
       _cons |   .0017426    .001593     1.09   0.274    -.0013796    .0048647
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L3. |  -.1276612    .057953    -2.20   0.028     -.241247   -.0140755
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0008072   .0000615    13.14   0.000     .0006868    .0009277
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    21.7570
 Prob > chi2(40)           =     0.9917

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    35.8304
 Prob > chi2(40)           =     0.6584

.           
.           eststo: arch returns_tjx, ma(3) het(sanct)

(setting optimization to BHHH)
Iteration 0:   log likelihood =   538.1469  
Iteration 1:   log likelihood =  541.68902  
Iteration 2:   log likelihood =  542.25092  
Iteration 3:   log likelihood =  542.42738  
Iteration 4:   log likelihood =  542.45603  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  542.46001  
Iteration 6:   log likelihood =   542.4604  
Iteration 7:   log likelihood =   542.4604  

Time-series regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 5783 - 6035                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       4.55
Log likelihood =  542.4604                        Prob > chi2     =     0.0329

------------------------------------------------------------------------------
             |                 OPG
 returns_tjx |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_tjx  |
       _cons |   .0014628   .0016043     0.91   0.362    -.0016816    .0046071
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L3. |  -.1227177   .0575093    -2.13   0.033    -.2354339   -.0100016
-------------+----------------------------------------------------------------
HET          |
       sanct |   .3342669    .259115     1.29   0.197    -.1735892     .842123
       _cons |  -7.152523   .0798754   -89.55   0.000    -7.309076    -6.99597
------------------------------------------------------------------------------
(est6 stored)

.                   
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    21.0373
 Prob > chi2(40)           =     0.9941

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    35.4671
 Prob > chi2(40)           =     0.6744

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sanct SanctionsThreat) nomtitl
> es title(TJX Companies 1991 (Germany)) nodep  

TJX Companies 1991 (Germany)
----------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)   
----------------------------------------------------------------------------------------
returns_~x                                                                              
Constant       0.002        0.002        0.002        0.002        0.002        0.001   
             (0.002)      (0.002)      (0.002)      (0.001)      (0.002)      (0.002)   
----------------------------------------------------------------------------------------
SIGMA2                                                                                  
Constant       0.001***     0.001***     0.001***     0.001***     0.001***             
             (0.000)      (0.000)      (0.000)      (0.000)      (0.000)                
----------------------------------------------------------------------------------------
ARMA                                                                                    
L.ar                       -0.024                     0.624*                            
                          (0.067)                   (0.355)                             
L.ma                                    -0.027       -0.708**                           
                                       (0.067)      (0.316)                             
L3.ma                                                             -0.128**     -0.123** 
                                                                 (0.058)      (0.058)   
----------------------------------------------------------------------------------------
HET                                                                                     
Sanction~t                                                                      0.334   
                                                                              (0.259)   
Constant                                                                       -7.153***
                                                                              (0.080)   
----------------------------------------------------------------------------------------
N                253          253          253          253          253          253   
aic        -1075.354    -1073.494    -1073.516    -1074.205    -1077.829    -1076.921   
bic        -1068.287    -1062.894    -1062.916    -1060.071    -1067.229    -1062.787   
----------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/tjx1991.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sa
> nct SanctionsThreat) nomtitles title(TJX Companies 1991 (Germany)) nodep replace
(note: file rawtables/tjx1991.tex not found)
(output written to rawtables/tjx1991.tex)

.                  
.   * Advanced Micro Devices, Inc. 1997 (Japan)
. 
.     * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen returns_amd  = ln(amd_close/amd_close[_n-1])        
(3,148 missing values generated)

. 
.         * Sanctions Variable    
.           gen sanc = 1 if date > td("25sep1997")
(7,486 missing values generated)

.           recode sanc(.=0)    
(sanc: 7486 changes made)

.           
.         * Limit Time Period
.           keep if date > td("31dec1996") & date < td("01jan1998")         
(12,841 observations deleted)

.           
.         * Set for analysis
.           tsset t
        time variable:  t, 7301 to 7553
                delta:  1 unit

.           
.         * Table A.27 Models
.           eststo clear

.           
.           eststo: arch returns_amd

(setting optimization to BHHH)
Iteration 0:   log likelihood =  440.72165  
Iteration 1:   log likelihood =  440.72165  

Time-series regression

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  440.7217                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 returns_amd |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |  -.0014705   .0026849    -0.55   0.584    -.0067328    .0037917
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0017966   .0000897    20.03   0.000     .0016208    .0019724
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    54.8208
 Prob > chi2(40)           =     0.0594

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    77.0933
 Prob > chi2(40)           =     0.0004

.           
.           eststo: arch returns_amd, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  441.52623  
Iteration 1:   log likelihood =  442.52907  
Iteration 2:   log likelihood =  442.64463  
Iteration 3:   log likelihood =  442.68417  
Iteration 4:   log likelihood =  442.69681  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  442.70101  
Iteration 6:   log likelihood =   442.7024  
Iteration 7:   log likelihood =  442.70242  

Time-series regression -- AR disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       7.71
Log likelihood =  442.7024                        Prob > chi2     =     0.0055

------------------------------------------------------------------------------
             |                 OPG
 returns_amd |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_amd  |
       _cons |  -.0015076   .0024903    -0.61   0.545    -.0063885    .0033732
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.1248342   .0449619    -2.78   0.005    -.2129579   -.0367105
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0017687   .0000894    19.79   0.000     .0015935    .0019439
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    47.8660
 Prob > chi2(40)           =     0.1838

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    64.5858
 Prob > chi2(40)           =     0.0082

.           
.           eststo: arch returns_amd, ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  441.73425  
Iteration 1:   log likelihood =  442.72581  
Iteration 2:   log likelihood =  442.87421  
Iteration 3:   log likelihood =  442.91867  
Iteration 4:   log likelihood =  442.93231  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  442.93664  
Iteration 6:   log likelihood =  442.93856  
Iteration 7:   log likelihood =  442.93861  

Time-series regression -- MA disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =      10.49
Log likelihood =  442.9386                        Prob > chi2     =     0.0012

------------------------------------------------------------------------------
             |                 OPG
 returns_amd |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_amd  |
       _cons |  -.0015183   .0023995    -0.63   0.527    -.0062212    .0031845
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |  -.1387222   .0428395    -3.24   0.001    -.2226861   -.0547583
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0017654   .0000889    19.85   0.000     .0015911    .0019397
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    47.6377
 Prob > chi2(40)           =     0.1899

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    62.8906
 Prob > chi2(40)           =     0.0119

.           
.           eststo: arch returns_amd, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  440.83458  
Iteration 1:   log likelihood =  442.63731  
Iteration 2:   log likelihood =  443.02962  
Iteration 3:   log likelihood =  443.18741  
Iteration 4:   log likelihood =  443.24608  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  443.26446  
Iteration 6:   log likelihood =  443.27381  
Iteration 7:   log likelihood =  443.27389  
Iteration 8:   log likelihood =  443.27389  

Time-series regression -- ARMA disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =      21.94
Log likelihood =  443.2739                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
 returns_amd |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_amd  |
       _cons |  -.0015585   .0021309    -0.73   0.465     -.005735     .002618
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .4638466   .3516063     1.32   0.187    -.2252891    1.152982
             |
          ma |
         L1. |  -.5904038   .3282097    -1.80   0.072    -1.233683    .0528753
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0017608   .0000886    19.88   0.000     .0015872    .0019344
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    48.8023
 Prob > chi2(40)           =     0.1603

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    63.5161
 Prob > chi2(40)           =     0.0104

.           
.           eststo: arch returns_amd, ar(1,4)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  444.61816  
Iteration 1:   log likelihood =  445.15947  
Iteration 2:   log likelihood =   445.3399  
Iteration 3:   log likelihood =  445.41483  
Iteration 4:   log likelihood =  445.43686  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  445.44437  
Iteration 6:   log likelihood =  445.44813  
Iteration 7:   log likelihood =  445.44823  
Iteration 8:   log likelihood =  445.44823  

Time-series regression -- AR disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =      12.23
Log likelihood =  445.4482                        Prob > chi2     =     0.0022

------------------------------------------------------------------------------
             |                 OPG
 returns_amd |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_amd  |
       _cons |  -.0015624   .0021933    -0.71   0.476    -.0058613    .0027364
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.1126423   .0444797    -2.53   0.011    -.1998209   -.0254638
         L4. |  -.1461866   .0649553    -2.25   0.024    -.2734967   -.0188765
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0017307    .000099    17.48   0.000     .0015367    .0019248
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.0021
 Prob > chi2(40)           =     0.4702

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    67.2421
 Prob > chi2(40)           =     0.0045

.           
.           eststo: arch returns_amd, ar(1,4) arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  447.22684  
Iteration 1:   log likelihood =   448.4592  
Iteration 2:   log likelihood =  448.74445  
Iteration 3:   log likelihood =  448.83581  
Iteration 4:   log likelihood =  448.86614  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  448.87507  
Iteration 6:   log likelihood =  448.87572  
Iteration 7:   log likelihood =  448.87591  
Iteration 8:   log likelihood =  448.87593  

ARCH family regression -- AR disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =       6.31
Log likelihood =  448.8759                        Prob > chi2     =     0.0426

------------------------------------------------------------------------------
             |                 OPG
 returns_amd |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_amd  |
       _cons |  -.0017862   .0023086    -0.77   0.439    -.0063109    .0027385
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.0398695   .0749062    -0.53   0.595    -.1866829    .1069439
         L4. |   -.147645   .0604371    -2.44   0.015    -.2660995   -.0291904
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1022463   .0570458     1.79   0.073    -.0095614     .214054
             |
       _cons |   .0015358   .0001168    13.14   0.000     .0013068    .0017648
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    38.3861
 Prob > chi2(40)           =     0.5430

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    65.9949
 Prob > chi2(40)           =     0.0060

.           
.           eststo: arch returns_amd, ar(1,4) arch(1) garch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  447.95861  
Iteration 1:   log likelihood =  449.36655  
Iteration 2:   log likelihood =  449.53389  
Iteration 3:   log likelihood =  449.68435  
Iteration 4:   log likelihood =  449.71504  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  449.72614  
Iteration 6:   log likelihood =   449.7357  
Iteration 7:   log likelihood =  449.73645  
Iteration 8:   log likelihood =  449.73658  
Iteration 9:   log likelihood =  449.73659  

ARCH family regression -- AR disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =       6.26
Log likelihood =  449.7366                        Prob > chi2     =     0.0436

------------------------------------------------------------------------------
             |                 OPG
 returns_amd |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_amd  |
       _cons |  -.0015561   .0022979    -0.68   0.498    -.0060599    .0029476
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.0364697   .0775854    -0.47   0.638    -.1885342    .1155949
         L4. |   -.171858   .0692337    -2.48   0.013    -.3075536   -.0361624
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1014116   .0531452     1.91   0.056     -.002751    .2055743
             |
       garch |
         L1. |   .4590936   .3481713     1.32   0.187    -.2233096    1.141497
             |
       _cons |   .0007517   .0005365     1.40   0.161    -.0002997    .0018031
------------------------------------------------------------------------------
(est7 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.5873
 Prob > chi2(40)           =     0.5794

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    66.9759
 Prob > chi2(40)           =     0.0048

.           
.           eststo: arch returns_amd, ar(1,4) arch(1, 17)

(setting optimization to BHHH)
Iteration 0:   log likelihood =   447.4302  
Iteration 1:   log likelihood =  448.59885  
Iteration 2:   log likelihood =  449.41187  
Iteration 3:   log likelihood =  449.73018  
Iteration 4:   log likelihood =  449.95684  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  450.11969  
Iteration 6:   log likelihood =  450.68701  
Iteration 7:   log likelihood =  450.72418  
Iteration 8:   log likelihood =  450.73172  
Iteration 9:   log likelihood =  450.73238  
Iteration 10:  log likelihood =  450.73244  
Iteration 11:  log likelihood =  450.73244  

ARCH family regression -- AR disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =       2.79
Log likelihood =  450.7324                        Prob > chi2     =     0.2475

------------------------------------------------------------------------------
             |                 OPG
 returns_amd |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_amd  |
       _cons |  -.0014476   .0024691    -0.59   0.558    -.0062869    .0033917
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0251495   .0669959     0.38   0.707      -.10616     .156459
         L4. |  -.0992055   .0597912    -1.66   0.097     -.216394    .0179831
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0844345    .048476     1.74   0.082    -.0105767    .1794458
        L17. |   .1834653   .1096317     1.67   0.094    -.0314088    .3983395
             |
       _cons |   .0012755   .0001355     9.41   0.000       .00101    .0015411
------------------------------------------------------------------------------
(est8 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    42.3730
 Prob > chi2(40)           =     0.3690

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    66.3517
 Prob > chi2(40)           =     0.0055

.           
.           eststo: arch returns_amd, ar(1,4) arch(1, 17) het(sanc)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  447.12606  
Iteration 1:   log likelihood =  450.19674  
Iteration 2:   log likelihood =  452.09884  
Iteration 3:   log likelihood =   452.2503  
Iteration 4:   log likelihood =    453.912  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  454.47959  
Iteration 6:   log likelihood =  455.49149  
Iteration 7:   log likelihood =  456.09253  
Iteration 8:   log likelihood =  456.23558  
Iteration 9:   log likelihood =   456.2529  
Iteration 10:  log likelihood =  456.25406  
Iteration 11:  log likelihood =  456.25413  
Iteration 12:  log likelihood =  456.25414  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =       4.43
Log likelihood =  456.2541                        Prob > chi2     =     0.1090

------------------------------------------------------------------------------
             |                 OPG
 returns_amd |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_amd  |
       _cons |  -.0006281   .0023003    -0.27   0.785    -.0051367    .0038805
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.0202379   .0714815    -0.28   0.777    -.1603391    .1198634
         L4. |  -.1444771   .0690486    -2.09   0.036      -.27981   -.0091442
-------------+----------------------------------------------------------------
HET          |
        sanc |   .7881711   .1584847     4.97   0.000     .4775469    1.098795
       _cons |  -6.754518   .1182246   -57.13   0.000    -6.986234   -6.522802
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0568373   .0515244     1.10   0.270    -.0441486    .1578232
        L17. |   .0441845   .0909965     0.49   0.627    -.1341653    .2225343
------------------------------------------------------------------------------
(est9 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    35.0124
 Prob > chi2(40)           =     0.6940

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    49.5340
 Prob > chi2(40)           =     0.1435

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sanc Sanctions) nomtitles titl
> e(Advanced Micro Devices, Inc. 1997 (Japan)) nodep  

Advanced Micro Devices, Inc. 1997 (Japan)
-------------------------------------------------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)          (7)          (8)          (9)   
-------------------------------------------------------------------------------------------------------------------------------
returns_~d                                                                                                                     
Constant      -0.001       -0.002       -0.002       -0.002       -0.002       -0.002       -0.002       -0.001       -0.001   
             (0.003)      (0.002)      (0.002)      (0.002)      (0.002)      (0.002)      (0.002)      (0.002)      (0.002)   
-------------------------------------------------------------------------------------------------------------------------------
SIGMA2                                                                                                                         
Constant       0.002***     0.002***     0.002***     0.002***     0.002***                                                    
             (0.000)      (0.000)      (0.000)      (0.000)      (0.000)                                                       
-------------------------------------------------------------------------------------------------------------------------------
ARMA                                                                                                                           
L.ar                       -0.125***                  0.464       -0.113**     -0.040       -0.036        0.025       -0.020   
                          (0.045)                   (0.352)      (0.044)      (0.075)      (0.078)      (0.067)      (0.071)   
L4.ar                                                             -0.146**     -0.148**     -0.172**     -0.099*      -0.144** 
                                                                 (0.065)      (0.060)      (0.069)      (0.060)      (0.069)   
L.ma                                    -0.139***    -0.590*                                                                   
                                       (0.043)      (0.328)                                                                    
-------------------------------------------------------------------------------------------------------------------------------
ARCH                                                                                                                           
L.arch                                                                          0.102*       0.101*       0.084*       0.057   
                                                                              (0.057)      (0.053)      (0.048)      (0.052)   
L17.arch                                                                                                  0.183*       0.044   
                                                                                                        (0.110)      (0.091)   
L.garch                                                                                      0.459                             
                                                                                           (0.348)                             
Constant                                                                        0.002***     0.001        0.001***             
                                                                              (0.000)      (0.001)      (0.000)                
-------------------------------------------------------------------------------------------------------------------------------
HET                                                                                                                            
Sanctions                                                                                                              0.788***
                                                                                                                     (0.158)   
Constant                                                                                                              -6.755***
                                                                                                                     (0.118)   
-------------------------------------------------------------------------------------------------------------------------------
N                253          253          253          253          253          253          253          253          253   
aic         -877.443     -879.405     -879.877     -878.548     -882.896     -887.752     -887.473     -889.465     -898.508   
bic         -870.377     -868.805     -869.277     -864.414     -868.763     -870.085     -866.273     -868.265     -873.775   
-------------------------------------------------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/amd1997.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sa
> nc Sanctions) nomtitles title(Advanced Micro Devices, Inc. 1997 (Japan)) nodep replace
(note: file rawtables/amd1997.tex not found)
(output written to rawtables/amd1997.tex)

. 
.   * Analog Devices, Inc. 1997 (Japan)
.   
.     * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen returns_adi  = ln(adi_close/adi_close[_n-1])        
(3,148 missing values generated)

. 
.         * Sanctions Variable    
.           gen sanc = 1 if date > td("25sep1997")
(7,486 missing values generated)

.           recode sanc(.=0)    
(sanc: 7486 changes made)

.           
.         * Limit Time Period
.           keep if date > td("31dec1996") & date < td("01jan1998")         
(12,841 observations deleted)

.           
.         * Set for analysis
.           tsset t
        time variable:  t, 7301 to 7553
                delta:  1 unit

.           
.         * Table A.28 Models
.           eststo clear

.           
.           eststo: arch returns_adi

(setting optimization to BHHH)
Iteration 0:   log likelihood =  488.57098  
Iteration 1:   log likelihood =  488.57098  

Time-series regression

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =   488.571                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 returns_adi |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .0003399   .0022398     0.15   0.879    -.0040502    .0047299
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0012308   .0000601    20.49   0.000      .001113    .0013485
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    27.2354
 Prob > chi2(40)           =     0.9381

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    44.2097
 Prob > chi2(40)           =     0.2984

.           
.           eststo: arch returns_adi, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  489.23729  
Iteration 1:   log likelihood =  489.61734  
Iteration 2:   log likelihood =  489.67669  
Iteration 3:   log likelihood =  489.68979  
Iteration 4:   log likelihood =  489.69229  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  489.69311  
Iteration 6:   log likelihood =  489.69359  
Iteration 7:   log likelihood =  489.69361  

Time-series regression -- AR disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       5.45
Log likelihood =  489.6936                        Prob > chi2     =     0.0195

------------------------------------------------------------------------------
             |                 OPG
 returns_adi |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_adi  |
       _cons |   .0003475   .0020706     0.17   0.867    -.0037107    .0044058
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.0940216   .0402562    -2.34   0.020    -.1729224   -.0151208
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0012199   .0000681    17.91   0.000     .0010864    .0013534
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    24.2666
 Prob > chi2(40)           =     0.9765

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    41.9790
 Prob > chi2(40)           =     0.3851

.           
.           eststo: arch returns_adi, ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  489.38449  
Iteration 1:   log likelihood =  489.78566  
Iteration 2:   log likelihood =  489.83668  
Iteration 3:   log likelihood =  489.86094  
Iteration 4:   log likelihood =  489.86777  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  489.86908  
Iteration 6:   log likelihood =  489.86992  
Iteration 7:   log likelihood =  489.86998  
Iteration 8:   log likelihood =  489.86998  

Time-series regression -- MA disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       8.25
Log likelihood =    489.87                        Prob > chi2     =     0.0041

------------------------------------------------------------------------------
             |                 OPG
 returns_adi |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_adi  |
       _cons |   .0003439   .0020296     0.17   0.865    -.0036342    .0043219
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |    -.10933   .0380582    -2.87   0.004    -.1839227   -.0347374
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0012182   .0000664    18.33   0.000      .001088    .0013484
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    23.8456
 Prob > chi2(40)           =     0.9799

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    41.7214
 Prob > chi2(40)           =     0.3958

.           
.           eststo: arch returns_adi, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  489.34302  
Iteration 1:   log likelihood =  490.23456  
Iteration 2:   log likelihood =  490.76505  
Iteration 3:   log likelihood =  490.90188  
Iteration 4:   log likelihood =  490.95384  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  490.97576  
Iteration 6:   log likelihood =  490.98648  
Iteration 7:   log likelihood =  490.98998  
Iteration 8:   log likelihood =  490.99008  
Iteration 9:   log likelihood =  490.99008  

Time-series regression -- ARMA disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =      79.84
Log likelihood =  490.9901                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
 returns_adi |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_adi  |
       _cons |   .0002361    .001549     0.15   0.879       -.0028    .0032721
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .7384036   .1830327     4.03   0.000      .379666    1.097141
             |
          ma |
         L1. |   -.832335   .1636831    -5.09   0.000    -1.153148    -.511522
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0012074    .000062    19.47   0.000     .0010858     .001329
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    22.0301
 Prob > chi2(40)           =     0.9906

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    46.8191
 Prob > chi2(40)           =     0.2129

.           
.           eststo: arch returns_adi, ar(1) ma(1) arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  492.50866  
Iteration 1:   log likelihood =  498.50062  
Iteration 2:   log likelihood =   499.1158  
Iteration 3:   log likelihood =  499.57996  
Iteration 4:   log likelihood =   499.6215  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  499.62534  
Iteration 6:   log likelihood =  499.62716  
Iteration 7:   log likelihood =  499.62752  
Iteration 8:   log likelihood =  499.62762  
Iteration 9:   log likelihood =  499.62763  

ARCH family regression -- ARMA disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =      99.84
Log likelihood =  499.6276                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
 returns_adi |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_adi  |
       _cons |   .0003183   .0014284     0.22   0.824    -.0024812    .0031179
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .7958629   .1729285     4.60   0.000     .4569292    1.134797
             |
          ma |
         L1. |  -.8750481   .1369263    -6.39   0.000    -1.143419   -.6066775
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |    .172968   .0884337     1.96   0.050     -.000359    .3462949
             |
       _cons |   .0009652   .0000924    10.45   0.000     .0007841    .0011463
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    20.7042
 Prob > chi2(40)           =     0.9950

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    19.9397
 Prob > chi2(40)           =     0.9967

.           
.           eststo: arch returns_adi, ar(1) ma(1) arch(1) het(sanc)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  498.67156  
Iteration 1:   log likelihood =  501.24175  
Iteration 2:   log likelihood =  501.94811  
Iteration 3:   log likelihood =  502.15987  
Iteration 4:   log likelihood =  502.27381  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  502.30873  
Iteration 6:   log likelihood =  502.34783  
Iteration 7:   log likelihood =  502.35184  
Iteration 8:   log likelihood =  502.35322  
Iteration 9:   log likelihood =  502.35332  
Iteration 10:  log likelihood =  502.35333  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =      85.89
Log likelihood =  502.3533                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
 returns_adi |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_adi  |
       _cons |   .0004643   .0014302     0.32   0.745     -.002339    .0032675
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .8004117   .1789414     4.47   0.000     .4496931     1.15113
             |
          ma |
         L1. |   -.874741   .1454198    -6.02   0.000    -1.159759   -.5897235
-------------+----------------------------------------------------------------
HET          |
        sanc |   .5180118   .1493973     3.47   0.001     .2251985    .8108251
       _cons |  -7.069106   .1126476   -62.75   0.000    -7.289892   -6.848321
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1334172   .0692705     1.93   0.054    -.0023504    .2691848
------------------------------------------------------------------------------
(est6 stored)

.   
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    21.7547
 Prob > chi2(40)           =     0.9917

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    26.7892
 Prob > chi2(40)           =     0.9456

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sanc Sanctions) nomtitles titl
> e(Analog Devices, Inc. 1997 (Japan)) nodep  

Analog Devices, Inc. 1997 (Japan)
----------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)   
----------------------------------------------------------------------------------------
returns_~i                                                                              
Constant       0.000        0.000        0.000        0.000        0.000        0.000   
             (0.002)      (0.002)      (0.002)      (0.002)      (0.001)      (0.001)   
----------------------------------------------------------------------------------------
SIGMA2                                                                                  
Constant       0.001***     0.001***     0.001***     0.001***                          
             (0.000)      (0.000)      (0.000)      (0.000)                             
----------------------------------------------------------------------------------------
ARMA                                                                                    
L.ar                       -0.094**                   0.738***     0.796***     0.800***
                          (0.040)                   (0.183)      (0.173)      (0.179)   
L.ma                                    -0.109***    -0.832***    -0.875***    -0.875***
                                       (0.038)      (0.164)      (0.137)      (0.145)   
----------------------------------------------------------------------------------------
ARCH                                                                                    
L.arch                                                             0.173*       0.133*  
                                                                 (0.088)      (0.069)   
Constant                                                           0.001***             
                                                                 (0.000)                
----------------------------------------------------------------------------------------
HET                                                                                     
Sanctions                                                                       0.518***
                                                                              (0.149)   
Constant                                                                       -7.069***
                                                                              (0.113)   
----------------------------------------------------------------------------------------
N                253          253          253          253          253          253   
aic         -973.142     -973.387     -973.740     -973.980     -989.255     -992.707   
bic         -966.075     -962.787     -963.140     -959.847     -971.588     -971.506   
----------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/adi1997.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sa
> nc Sanctions) nomtitles title(Analog Devices, Inc. 1997 (Japan)) nodep replace
(note: file rawtables/adi1997.tex not found)
(output written to rawtables/adi1997.tex)

.   
.   * Intel Corporation 1997 (Japan)
.   
.     * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen returns_intc = ln(intc_close/intc_close[_n-1])              
(3,726 missing values generated)

. 
.         * Sanctions Variable    
.           gen sanc = 1 if date > td("25sep1997")
(7,486 missing values generated)

.           recode sanc(.=0)    
(sanc: 7486 changes made)

.           
.         * Limit Time Period
.           keep if date > td("31dec1996") & date < td("01jan1998")         
(12,841 observations deleted)

.           
.         * Set for analysis
.           tsset t
        time variable:  t, 7301 to 7553
                delta:  1 unit

.           
.         * Table A.29 Models
.           eststo clear  

.           
.           eststo: arch returns_intc

(setting optimization to BHHH)
Iteration 0:   log likelihood =  578.50611  
Iteration 1:   log likelihood =  578.50611  

Time-series regression

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  578.5061                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
returns_intc |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .0002786   .0015771     0.18   0.860    -.0028124    .0033696
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0006045    .000035    17.26   0.000     .0005359    .0006732
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    33.7281
 Prob > chi2(40)           =     0.7473

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    42.4235
 Prob > chi2(40)           =     0.3670

.           
.           eststo: arch returns_intc, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =   577.9547  
Iteration 1:   log likelihood =   578.4113  
Iteration 2:   log likelihood =  578.48009  
Iteration 3:   log likelihood =  578.49834  
Iteration 4:   log likelihood =  578.50368  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  578.50541  
Iteration 6:   log likelihood =  578.50613  
Iteration 7:   log likelihood =  578.50615  

Time-series regression -- AR disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       0.00
Log likelihood =  578.5061                        Prob > chi2     =     0.9912

------------------------------------------------------------------------------
             |                 OPG
returns_intc |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_intc |
       _cons |   .0002787   .0015824     0.18   0.860    -.0028228    .0033801
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0005057   .0459325     0.01   0.991    -.0895203    .0905317
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0006045   .0000375    16.13   0.000     .0005311     .000678
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    33.7378
 Prob > chi2(40)           =     0.7469

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    42.4430
 Prob > chi2(40)           =     0.3662

.           
.           eststo: arch returns_intc, ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =   577.8864  
Iteration 1:   log likelihood =   578.4642  
Iteration 2:   log likelihood =  578.49321  
Iteration 3:   log likelihood =  578.50215  
Iteration 4:   log likelihood =  578.50533  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  578.50609  
Iteration 6:   log likelihood =  578.50615  
Iteration 7:   log likelihood =  578.50615  

Time-series regression -- MA disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       0.00
Log likelihood =  578.5061                        Prob > chi2     =     0.9905

------------------------------------------------------------------------------
             |                 OPG
returns_intc |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_intc |
       _cons |   .0002785   .0015825     0.18   0.860    -.0028231    .0033801
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |   .0005462   .0459268     0.01   0.991    -.0894686     .090561
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0006045   .0000375    16.13   0.000     .0005311     .000678
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    33.7386
 Prob > chi2(40)           =     0.7469

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    42.4441
 Prob > chi2(40)           =     0.3661

.           
.           eststo: arch returns_intc, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  577.90726  
Iteration 1:   log likelihood =  578.21495  
Iteration 2:   log likelihood =  578.39885  
Iteration 3:   log likelihood =  578.49829  
Iteration 4:   log likelihood =  578.56136  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  578.56637  
Iteration 6:   log likelihood =  578.57127  
Iteration 7:   log likelihood =  578.57508  
Iteration 8:   log likelihood =  578.57534  
Iteration 9:   log likelihood =  578.57535  

Time-series regression -- ARMA disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =       0.58
Log likelihood =  578.5753                        Prob > chi2     =     0.7475

------------------------------------------------------------------------------
             |                 OPG
returns_intc |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_intc |
       _cons |   .0002744   .0015297     0.18   0.858    -.0027237    .0032725
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |     .56835    1.64663     0.35   0.730    -2.658985    3.795685
             |
          ma |
         L1. |  -.5876111   1.624499    -0.36   0.718     -3.77157    2.596348
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0006042    .000038    15.90   0.000     .0005297    .0006787
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    33.5225
 Prob > chi2(40)           =     0.7555

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    42.5407
 Prob > chi2(40)           =     0.3622

.           
.           eststo: arch returns_intc, het(sanc)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  581.19049  
Iteration 1:   log likelihood =    582.322  
Iteration 2:   log likelihood =   582.5227  
Iteration 3:   log likelihood =  582.56196  
Iteration 4:   log likelihood =  582.56778  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  582.56906  
Iteration 6:   log likelihood =  582.56946  
Iteration 7:   log likelihood =  582.56949  
Iteration 8:   log likelihood =  582.56949  

Time-series regression -- multiplicative heteroskedasticity

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  582.5695                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
returns_intc |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_intc |
       _cons |   .0008506   .0015164     0.56   0.575    -.0021215    .0038228
-------------+----------------------------------------------------------------
HET          |
        sanc |   .5570835   .1328085     4.19   0.000     .2967836    .8173834
       _cons |  -7.590699   .0856086   -88.67   0.000    -7.758489   -7.422909
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    32.0509
 Prob > chi2(40)           =     0.8105

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    35.0980
 Prob > chi2(40)           =     0.6903

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sanc Sanctions) nomtitles titl
> e(Intel Corporation 1997 (Japan)) nodep  

Intel Corporation 1997 (Japan)
---------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)   
---------------------------------------------------------------------------
returns_~c                                                                 
Constant       0.000        0.000        0.000        0.000        0.001   
             (0.002)      (0.002)      (0.002)      (0.002)      (0.002)   
---------------------------------------------------------------------------
SIGMA2                                                                     
Constant       0.001***     0.001***     0.001***     0.001***             
             (0.000)      (0.000)      (0.000)      (0.000)                
---------------------------------------------------------------------------
ARMA                                                                       
L.ar                        0.001                     0.568                
                          (0.046)                   (1.647)                
L.ma                                     0.001       -0.588                
                                       (0.046)      (1.624)                
---------------------------------------------------------------------------
HET                                                                        
Sanctions                                                          0.557***
                                                                 (0.133)   
Constant                                                          -7.591***
                                                                 (0.086)   
---------------------------------------------------------------------------
N                253          253          253          253          253   
aic        -1153.012    -1151.012    -1151.012    -1149.151    -1159.139   
bic        -1145.945    -1140.412    -1140.412    -1135.017    -1148.539   
---------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/intel1997.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant 
> sanc Sanctions) nomtitles title(Intel Corporation 1997 (Japan)) nodep replace 
(note: file rawtables/intel1997.tex not found)
(output written to rawtables/intel1997.tex)

. 
.   * Maxim Integrated Products Inc. 1997 (Japan)
.   
.     * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen returns_mxim = ln(mxim_close/mxim_close[_n-1])              
(5,088 missing values generated)

. 
.         * Sanctions Variable    
.           gen sanc = 1 if date > td("25sep1997")
(7,486 missing values generated)

.           recode sanc(.=0)    
(sanc: 7486 changes made)

.           
.         * Limit Time Period
.           keep if date > td("31dec1996") & date < td("01jan1998")         
(12,841 observations deleted)

.           
.         * Set for analysis
.           tsset t
        time variable:  t, 7301 to 7553
                delta:  1 unit

.           
.         * Table A.30 Models
.           eststo clear  

.           
.           eststo: arch returns_mxim

(setting optimization to BHHH)
Iteration 0:   log likelihood =  505.70498  
Iteration 1:   log likelihood =  505.70498  

Time-series regression

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =   505.705                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
returns_mxim |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .0018463    .002112     0.87   0.382    -.0022931    .0059857
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0010749   .0000868    12.38   0.000     .0009047     .001245
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    31.9180
 Prob > chi2(40)           =     0.8151

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    43.2035
 Prob > chi2(40)           =     0.3361

.           
.           eststo: arch returns_mxim, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  503.33513  
Iteration 1:   log likelihood =  505.62852  
Iteration 2:   log likelihood =   505.8066  
Iteration 3:   log likelihood =  505.81648  
Iteration 4:   log likelihood =   505.8169  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  505.81692  

Time-series regression -- AR disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       0.34
Log likelihood =  505.8169                        Prob > chi2     =     0.5608

------------------------------------------------------------------------------
             |                 OPG
returns_mxim |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mxim |
       _cons |   .0018516   .0021767     0.85   0.395    -.0024146    .0061177
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0298689   .0513454     0.58   0.561    -.0707662    .1305041
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0010739   .0000868    12.38   0.000     .0009038    .0012439
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    31.0704
 Prob > chi2(40)           =     0.8433

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    43.1368
 Prob > chi2(40)           =     0.3387

.           
.           eststo: arch returns_mxim, ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  503.18996  
Iteration 1:   log likelihood =  505.65562  
Iteration 2:   log likelihood =  505.85479  
Iteration 3:   log likelihood =  505.87448  
Iteration 4:   log likelihood =  505.87804  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  505.87872  
Iteration 6:   log likelihood =  505.87882  
Iteration 7:   log likelihood =  505.87882  

Time-series regression -- MA disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       0.78
Log likelihood =  505.8788                        Prob > chi2     =     0.3764

------------------------------------------------------------------------------
             |                 OPG
returns_mxim |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mxim |
       _cons |   .0018591    .002208     0.84   0.400    -.0024686    .0061867
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |   .0460614   .0520698     0.88   0.376    -.0559935    .1481163
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0010734   .0000867    12.37   0.000     .0009034    .0012434
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    30.6273
 Prob > chi2(40)           =     0.8570

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    43.1107
 Prob > chi2(40)           =     0.3397

.           
.           eststo: arch returns_mxim, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  503.20268  
Iteration 1:   log likelihood =   503.2279  (backed up)
Iteration 2:   log likelihood =  503.25146  (backed up)
Iteration 3:   log likelihood =  503.32152  (backed up)
Iteration 4:   log likelihood =  504.38577  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  506.58356  
Iteration 6:   log likelihood =  506.73095  (backed up)
Iteration 7:   log likelihood =  507.18902  
Iteration 8:   log likelihood =  507.18902  (backed up)
Iteration 9:   log likelihood =  507.23345  (backed up)
Iteration 10:  log likelihood =  507.72754  
Iteration 11:  log likelihood =  508.79439  
Iteration 12:  log likelihood =  508.86303  
Iteration 13:  log likelihood =  508.86462  
Iteration 14:  log likelihood =  508.86631  
(switching optimization to BHHH)
Iteration 15:  log likelihood =  509.15976  
Iteration 16:  log likelihood =   509.9753  
Iteration 17:  log likelihood =  510.19728  
Iteration 18:  log likelihood =  510.21461  
Iteration 19:  log likelihood =  510.22086  
(switching optimization to BFGS)
Iteration 20:  log likelihood =  510.22172  
Iteration 21:  log likelihood =  510.22193  
Iteration 22:  log likelihood =  510.22195  

Time-series regression -- ARMA disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =     258.72
Log likelihood =  510.2219                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
returns_mxim |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mxim |
       _cons |   .0016235   .0010186     1.59   0.111    -.0003729    .0036199
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .7618946   .1015798     7.50   0.000     .5628019    .9609874
             |
          ma |
         L1. |  -.8862396   .0748992   -11.83   0.000    -1.033039   -.7394399
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0010372    .000082    12.65   0.000     .0008765    .0011979
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    24.4574
 Prob > chi2(40)           =     0.9748

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.5882
 Prob > chi2(40)           =     0.5794

.           
.           eststo: arch returns_mxim, ar(2)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  508.22847  
Iteration 1:   log likelihood =  509.82349  
Iteration 2:   log likelihood =   509.9179  
Iteration 3:   log likelihood =  509.92261  
Iteration 4:   log likelihood =  509.92281  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  509.92283  

Time-series regression -- AR disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =      11.03
Log likelihood =  509.9228                        Prob > chi2     =     0.0009

------------------------------------------------------------------------------
             |                 OPG
returns_mxim |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mxim |
       _cons |   .0017893   .0017732     1.01   0.313    -.0016862    .0052648
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L2. |  -.1820058   .0548033    -3.32   0.001    -.2894182   -.0745933
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0010396   .0000833    12.48   0.000     .0008764    .0012028
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    26.8543
 Prob > chi2(40)           =     0.9446

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    42.0747
 Prob > chi2(40)           =     0.3812

.           
.           eststo: arch returns_mxim, ar(2) arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  513.16146  
Iteration 1:   log likelihood =  514.83951  
Iteration 2:   log likelihood =  514.99277  
Iteration 3:   log likelihood =  515.00534  
Iteration 4:   log likelihood =  515.00651  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  515.00662  
Iteration 6:   log likelihood =  515.00663  

ARCH family regression -- AR disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       8.56
Log likelihood =  515.0066                        Prob > chi2     =     0.0034

------------------------------------------------------------------------------
             |                 OPG
returns_mxim |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mxim |
       _cons |   .0020577   .0016268     1.26   0.206    -.0011308    .0052462
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L2. |  -.1613426   .0551493    -2.93   0.003    -.2694332    -.053252
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .2678183   .1096183     2.44   0.015     .0529703    .4826663
             |
       _cons |   .0007828   .0000932     8.40   0.000     .0006001    .0009654
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    25.2220
 Prob > chi2(40)           =     0.9670

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    31.5361
 Prob > chi2(40)           =     0.8281

.           
.           eststo: arch returns_mxim, ar(2) arch(1,17)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  513.85301  
Iteration 1:   log likelihood =  516.09517  
Iteration 2:   log likelihood =  516.32753  
Iteration 3:   log likelihood =  516.35929  
Iteration 4:   log likelihood =  516.36277  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  516.36322  
Iteration 6:   log likelihood =  516.36329  
Iteration 7:   log likelihood =  516.36329  

ARCH family regression -- AR disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       8.19
Log likelihood =  516.3633                        Prob > chi2     =     0.0042

------------------------------------------------------------------------------
             |                 OPG
returns_mxim |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mxim |
       _cons |   .0019553   .0015853     1.23   0.217    -.0011517    .0050624
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L2. |  -.1557001   .0543945    -2.86   0.004    -.2623113   -.0490889
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .2946472   .1095677     2.69   0.007     .0798986    .5093959
        L17. |   .0945184   .0771204     1.23   0.220    -.0566348    .2456716
             |
       _cons |   .0006683   .0000964     6.94   0.000     .0004794    .0008572
------------------------------------------------------------------------------
(est7 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    23.6737
 Prob > chi2(40)           =     0.9812

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    30.5815
 Prob > chi2(40)           =     0.8584

.           
.           eststo: arch returns_mxim, ar(2) arch(1,17) het(sanc)

(setting optimization to BHHH)
Iteration 0:   log likelihood =    515.905  
Iteration 1:   log likelihood =  517.93035  
Iteration 2:   log likelihood =  518.29056  
Iteration 3:   log likelihood =  518.33637  
Iteration 4:   log likelihood =  518.34183  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  518.34261  
Iteration 6:   log likelihood =  518.34273  
Iteration 7:   log likelihood =  518.34273  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       6.61
Log likelihood =  518.3427                        Prob > chi2     =     0.0101

------------------------------------------------------------------------------
             |                 OPG
returns_mxim |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mxim |
       _cons |   .0019474   .0014854     1.31   0.190    -.0009638    .0048586
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L2. |  -.1492708   .0580451    -2.57   0.010    -.2630371   -.0355045
-------------+----------------------------------------------------------------
HET          |
        sanc |   .5379213   .2616611     2.06   0.040      .025075    1.050768
       _cons |  -7.506571   .1680478   -44.67   0.000    -7.835939   -7.177204
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .2880152   .1043488     2.76   0.006     .0834954     .492535
        L17. |   .1233306   .0792061     1.56   0.119    -.0319106    .2785717
------------------------------------------------------------------------------
(est8 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    25.7471
 Prob > chi2(40)           =     0.9607

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    24.7635
 Prob > chi2(40)           =     0.9718

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sanc Sanctions) nomtitles titl
> e(Maxim Integrated Products Inc. 1997 (Japan)) nodep  

Maxim Integrated Products Inc. 1997 (Japan)
------------------------------------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)          (7)          (8)   
------------------------------------------------------------------------------------------------------------------
returns_~m                                                                                                        
Constant       0.002        0.002        0.002        0.002        0.002        0.002        0.002        0.002   
             (0.002)      (0.002)      (0.002)      (0.001)      (0.002)      (0.002)      (0.002)      (0.001)   
------------------------------------------------------------------------------------------------------------------
SIGMA2                                                                                                            
Constant       0.001***     0.001***     0.001***     0.001***     0.001***                                       
             (0.000)      (0.000)      (0.000)      (0.000)      (0.000)                                          
------------------------------------------------------------------------------------------------------------------
ARMA                                                                                                              
L.ar                        0.030                     0.762***                                                    
                          (0.051)                   (0.102)                                                       
L2.ar                                                             -0.182***    -0.161***    -0.156***    -0.149** 
                                                                 (0.055)      (0.055)      (0.054)      (0.058)   
L.ma                                     0.046       -0.886***                                                    
                                       (0.052)      (0.075)                                                       
------------------------------------------------------------------------------------------------------------------
ARCH                                                                                                              
L.arch                                                                          0.268**      0.295***     0.288***
                                                                              (0.110)      (0.110)      (0.104)   
L17.arch                                                                                     0.095        0.123   
                                                                                           (0.077)      (0.079)   
Constant                                                                        0.001***     0.001***             
                                                                              (0.000)      (0.000)                
------------------------------------------------------------------------------------------------------------------
HET                                                                                                               
Sanctions                                                                                                 0.538** 
                                                                                                        (0.262)   
Constant                                                                                                 -7.507***
                                                                                                        (0.168)   
------------------------------------------------------------------------------------------------------------------
N                253          253          253          253          253          253          253          253   
aic        -1007.410    -1005.634    -1005.758    -1012.444    -1013.846    -1022.013    -1022.727    -1024.685   
bic        -1000.343     -995.034     -995.157     -998.310    -1003.245    -1007.880    -1005.060    -1003.485   
------------------------------------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/mxim1997.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant s
> anc Sanctions) nomtitles title(Maxim Integrated Products Inc. 1997 (Japan)) nodep replace        
(note: file rawtables/mxim1997.tex not found)
(output written to rawtables/mxim1997.tex)

. 
.   * Microchip Technology Inc. 1997 (Japan)        
.   
.     * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen returns_mchp = ln(mchp_close/mchp_close[_n-1])      
(6,344 missing values generated)

. 
.         * Sanctions Variable    
.           gen sanc = 1 if date > td("25sep1997")
(7,486 missing values generated)

.           recode sanc(.=0)    
(sanc: 7486 changes made)

.           
.         * Limit Time Period
.           keep if date > td("31dec1996") & date < td("01jan1998")         
(12,841 observations deleted)

.           
.         * Set for analysis
.           tsset t
        time variable:  t, 7301 to 7553
                delta:  1 unit

.           
.         * Table A.31 Models
.           eststo clear  

.           
.           eststo: arch returns_mchp

(setting optimization to BHHH)
Iteration 0:   log likelihood =  455.30437  
Iteration 1:   log likelihood =  455.30437  

Time-series regression

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  455.3044                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
returns_mchp |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   -.000485   .0025835    -0.19   0.851    -.0055487    .0045786
-------------+----------------------------------------------------------------
     /SIGMA2 |    .001601   .0000865    18.50   0.000     .0014314    .0017706
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    46.2850
 Prob > chi2(40)           =     0.2289

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.4959
 Prob > chi2(40)           =     0.4484

.           
.           eststo: arch returns_mchp, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  454.42104  
Iteration 1:   log likelihood =  455.78229  
Iteration 2:   log likelihood =  455.97499  
Iteration 3:   log likelihood =  456.02553  
Iteration 4:   log likelihood =  456.03439  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  456.03717  
Iteration 6:   log likelihood =  456.03842  
Iteration 7:   log likelihood =  456.03844  

Time-series regression -- AR disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       1.93
Log likelihood =  456.0384                        Prob > chi2     =     0.1648

------------------------------------------------------------------------------
             |                 OPG
returns_mchp |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mchp |
       _cons |    -.00051   .0024411    -0.21   0.834    -.0052944    .0042744
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.0763293   .0549531    -1.39   0.165    -.1840355    .0313769
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0015917   .0000859    18.53   0.000     .0014234      .00176
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    45.1672
 Prob > chi2(40)           =     0.2649

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.0298
 Prob > chi2(40)           =     0.4689

.           
.           eststo: arch returns_mchp, ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  454.37853  
Iteration 1:   log likelihood =  455.89804  
Iteration 2:   log likelihood =  456.08144  
Iteration 3:   log likelihood =  456.11956  
Iteration 4:   log likelihood =  456.13243  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  456.13532  
Iteration 6:   log likelihood =  456.13617  
Iteration 7:   log likelihood =  456.13617  

Time-series regression -- MA disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       2.45
Log likelihood =  456.1362                        Prob > chi2     =     0.1179

------------------------------------------------------------------------------
             |                 OPG
returns_mchp |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mchp |
       _cons |  -.0005158   .0024053    -0.21   0.830    -.0052301    .0041985
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |  -.0862427   .0551491    -1.56   0.118    -.1943328    .0218475
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0015905   .0000858    18.55   0.000     .0014224    .0017586
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    44.2053
 Prob > chi2(40)           =     0.2985

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    39.9190
 Prob > chi2(40)           =     0.4739

.           
.           eststo: arch returns_mchp, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =   454.2649  
Iteration 1:   log likelihood =  455.01261  
Iteration 2:   log likelihood =  455.97497  
Iteration 3:   log likelihood =  456.01817  
Iteration 4:   log likelihood =  456.24742  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  456.26987  
Iteration 6:   log likelihood =  456.29072  
Iteration 7:   log likelihood =  456.29181  
Iteration 8:   log likelihood =  456.29185  

Time-series regression -- ARMA disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =       3.09
Log likelihood =  456.2919                        Prob > chi2     =     0.2134

------------------------------------------------------------------------------
             |                 OPG
returns_mchp |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mchp |
       _cons |  -.0005326    .002338    -0.23   0.820    -.0051151    .0040499
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .2525231   .6491491     0.39   0.697    -1.019786    1.524832
             |
          ma |
         L1. |   -.338584   .6371826    -0.53   0.595    -1.587439    .9102709
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0015886   .0000857    18.54   0.000     .0014207    .0017566
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    41.8815
 Prob > chi2(40)           =     0.3892

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    39.7349
 Prob > chi2(40)           =     0.4821

.           
.           eststo: arch returns_mchp, arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  455.60985  
Iteration 1:   log likelihood =  455.61424  
Iteration 2:   log likelihood =   455.6424  
Iteration 3:   log likelihood =  455.64383  
Iteration 4:   log likelihood =  455.64495  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  455.64526  
Iteration 6:   log likelihood =  455.64552  
Iteration 7:   log likelihood =  455.64554  

ARCH family regression

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  455.6455                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
returns_mchp |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mchp |
       _cons |  -.0003536   .0025561    -0.14   0.890    -.0053635    .0046563
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0296884   .0596199     0.50   0.619    -.0871644    .1465413
             |
       _cons |   .0015525   .0000967    16.05   0.000     .0013629    .0017421
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    44.6355
 Prob > chi2(40)           =     0.2832

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.6872
 Prob > chi2(40)           =     0.4400

.           
.           eststo: arch returns_mchp, arch(14)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  456.21775  
Iteration 1:   log likelihood =  458.39538  
Iteration 2:   log likelihood =  458.98138  
Iteration 3:   log likelihood =  459.24713  
Iteration 4:   log likelihood =  459.26453  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  459.26595  
Iteration 6:   log likelihood =  459.26655  
Iteration 7:   log likelihood =  459.26657  

ARCH family regression

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  459.2666                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
returns_mchp |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mchp |
       _cons |  -.0015137   .0027079    -0.56   0.576    -.0068211    .0037936
-------------+----------------------------------------------------------------
ARCH         |
        arch |
        L14. |   .0961785   .0554632     1.73   0.083    -.0125274    .2048845
             |
       _cons |   .0014289   .0000995    14.36   0.000      .001234    .0016239
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    43.7563
 Prob > chi2(40)           =     0.3151

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    32.9889
 Prob > chi2(40)           =     0.7761

.           
.           eststo: arch returns_mchp, het(sanc)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  447.71638  
Iteration 1:   log likelihood =  453.90946  
Iteration 2:   log likelihood =  455.23645  
Iteration 3:   log likelihood =  455.72878  
Iteration 4:   log likelihood =  455.91329  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  455.96324  
Iteration 6:   log likelihood =  455.98769  
Iteration 7:   log likelihood =  455.98857  
Iteration 8:   log likelihood =  455.98857  

Time-series regression -- multiplicative heteroskedasticity

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  455.9886                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
returns_mchp |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mchp |
       _cons |  -.0001318   .0026151    -0.05   0.960    -.0052573    .0049936
-------------+----------------------------------------------------------------
HET          |
        sanc |   .2332745   .1382303     1.69   0.091     -.037652     .504201
       _cons |  -6.504306   .0573289  -113.46   0.000    -6.616669   -6.391944
------------------------------------------------------------------------------
(est7 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    51.0019
 Prob > chi2(40)           =     0.1140

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    45.8515
 Prob > chi2(40)           =     0.2424

.           
.           eststo: arch returns_mchp, arch(14) het(sanc)

(setting optimization to BHHH)
Iteration 0:   log likelihood =   457.4865  
Iteration 1:   log likelihood =  458.05374  
Iteration 2:   log likelihood =  459.38295  
Iteration 3:   log likelihood =  460.05751  
Iteration 4:   log likelihood =  460.37124  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  460.44899  
Iteration 6:   log likelihood =  460.46397  
Iteration 7:   log likelihood =  460.46514  
Iteration 8:   log likelihood =  460.46542  
Iteration 9:   log likelihood =  460.46544  

ARCH family regression -- multiplicative heteroskedasticity

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  460.4654                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
returns_mchp |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mchp |
       _cons |  -.0009749   .0027773    -0.35   0.726    -.0064184    .0044686
-------------+----------------------------------------------------------------
HET          |
        sanc |   .3263337    .146286     2.23   0.026     .0396184     .613049
       _cons |  -6.651314   .0703817   -94.50   0.000     -6.78926   -6.513369
-------------+----------------------------------------------------------------
ARCH         |
        arch |
        L14. |   .0994197   .0546821     1.82   0.069    -.0077553    .2065947
------------------------------------------------------------------------------
(est8 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    49.5603
 Prob > chi2(40)           =     0.1429

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    38.4140
 Prob > chi2(40)           =     0.5418

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sanc Sanctions) nomtitles titl
> e(Microchip Technology Inc. 1997 (Japan)) nodep  

Microchip Technology Inc. 1997 (Japan)
------------------------------------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)          (7)          (8)   
------------------------------------------------------------------------------------------------------------------
returns_~p                                                                                                        
Constant      -0.000       -0.001       -0.001       -0.001       -0.000       -0.002       -0.000       -0.001   
             (0.003)      (0.002)      (0.002)      (0.002)      (0.003)      (0.003)      (0.003)      (0.003)   
------------------------------------------------------------------------------------------------------------------
SIGMA2                                                                                                            
Constant       0.002***     0.002***     0.002***     0.002***                                                    
             (0.000)      (0.000)      (0.000)      (0.000)                                                       
------------------------------------------------------------------------------------------------------------------
ARMA                                                                                                              
L.ar                       -0.076                     0.253                                                       
                          (0.055)                   (0.649)                                                       
L.ma                                    -0.086       -0.339                                                       
                                       (0.055)      (0.637)                                                       
------------------------------------------------------------------------------------------------------------------
ARCH                                                                                                              
L.arch                                                             0.030                                          
                                                                 (0.060)                                          
L14.arch                                                                        0.096*                    0.099*  
                                                                              (0.055)                   (0.055)   
Constant                                                           0.002***     0.001***                          
                                                                 (0.000)      (0.000)                             
------------------------------------------------------------------------------------------------------------------
HET                                                                                                               
Sanctions                                                                                    0.233*       0.326** 
                                                                                           (0.138)      (0.146)   
Constant                                                                                    -6.504***    -6.651***
                                                                                           (0.057)      (0.070)   
------------------------------------------------------------------------------------------------------------------
N                253          253          253          253          253          253          253          253   
aic         -906.609     -906.077     -906.272     -904.584     -905.291     -912.533     -905.977     -912.931   
bic         -899.542     -895.477     -895.672     -890.450     -894.691     -901.933     -895.377     -898.797   
------------------------------------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/mchp1997.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant s
> anc Sanctions) nomtitles title(Microchip Technology Inc. 1997 (Japan)) nodep replace     
(note: file rawtables/mchp1997.tex not found)
(output written to rawtables/mchp1997.tex)

.           
.   * Micron Technology, Inc. 1997 (Japan)          
.   
.     * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen returns_mu   = ln(mu_close/mu_close[_n-1])                  
(4,164 missing values generated)

. 
.         * Sanctions Variable    
.           gen sanc = 1 if date > td("25sep1997")
(7,486 missing values generated)

.           recode sanc(.=0)    
(sanc: 7486 changes made)

.           
.         * Limit Time Period
.           keep if date > td("31dec1996") & date < td("01jan1998")         
(12,841 observations deleted)

.           
.         * Set for analysis
.           tsset t
        time variable:  t, 7301 to 7553
                delta:  1 unit

.           
.         * Table A.32 Models
.           eststo clear  

.           
.           eststo: arch returns_mu

(setting optimization to BHHH)
Iteration 0:   log likelihood =  466.52949  
Iteration 1:   log likelihood =  466.52949  

Time-series regression

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  466.5295                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
  returns_mu |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |  -.0004581   .0024065    -0.19   0.849    -.0051749    .0042586
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0014651   .0000975    15.02   0.000     .0012739    .0016562
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.6943
 Prob > chi2(40)           =     0.5745

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    33.2537
 Prob > chi2(40)           =     0.7660

.           
.           eststo: arch returns_mu, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =   466.2041  
Iteration 1:   log likelihood =  466.62129  
Iteration 2:   log likelihood =  466.64583  
Iteration 3:   log likelihood =  466.64964  
Iteration 4:   log likelihood =  466.65041  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  466.65054  
Iteration 6:   log likelihood =  466.65056  

Time-series regression -- AR disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       0.37
Log likelihood =  466.6506                        Prob > chi2     =     0.5426

------------------------------------------------------------------------------
             |                 OPG
  returns_mu |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mu   |
       _cons |  -.0004529   .0025629    -0.18   0.860    -.0054761    .0045704
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0309402   .0508202     0.61   0.543    -.0686656    .1305461
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0014636   .0001012    14.46   0.000     .0012653     .001662
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.7501
 Prob > chi2(40)           =     0.5720

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    36.3107
 Prob > chi2(40)           =     0.6371

.           
.           eststo: arch returns_mu, ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  466.08242  
Iteration 1:   log likelihood =  466.61982  
Iteration 2:   log likelihood =  466.65355  
Iteration 3:   log likelihood =  466.65587  
Iteration 4:   log likelihood =  466.65609  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  466.65612  
Iteration 6:   log likelihood =  466.65613  

Time-series regression -- MA disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       0.41
Log likelihood =  466.6561                        Prob > chi2     =     0.5231

------------------------------------------------------------------------------
             |                 OPG
  returns_mu |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mu   |
       _cons |  -.0004545   .0025601    -0.18   0.859    -.0054721    .0045631
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |   .0323925   .0507311     0.64   0.523    -.0670387    .1318237
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0014636    .000101    14.49   0.000     .0012657    .0016615
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.7679
 Prob > chi2(40)           =     0.5712

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    36.4676
 Prob > chi2(40)           =     0.6301

.           
.           eststo: arch returns_mu, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  466.15137  
Iteration 1:   log likelihood =  466.83436  
Iteration 2:   log likelihood =  466.86851  
Iteration 3:   log likelihood =  466.89442  
Iteration 4:   log likelihood =  466.90843  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  466.91152  
Iteration 6:   log likelihood =   466.9141  
Iteration 7:   log likelihood =  466.91432  
Iteration 8:   log likelihood =  466.91433  

Time-series regression -- ARMA disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =       3.92
Log likelihood =  466.9143                        Prob > chi2     =     0.1408

------------------------------------------------------------------------------
             |                 OPG
  returns_mu |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mu   |
       _cons |  -.0004635   .0025063    -0.18   0.853    -.0053757    .0044487
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.6243733   .7134236    -0.88   0.381    -2.022658    .7739112
             |
          ma |
         L1. |   .6674747   .6853702     0.97   0.330    -.6758263    2.010776
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0014606   .0000999    14.62   0.000     .0012648    .0016564
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    36.2990
 Prob > chi2(40)           =     0.6376

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    38.2048
 Prob > chi2(40)           =     0.5513

.           
.           eststo: arch returns_mu, arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  472.63225  
Iteration 1:   log likelihood =  473.17533  
Iteration 2:   log likelihood =  473.27773  
Iteration 3:   log likelihood =  473.29066  
Iteration 4:   log likelihood =  473.29285  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  473.29319  
Iteration 6:   log likelihood =  473.29326  
Iteration 7:   log likelihood =  473.29326  

ARCH family regression

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  473.2933                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
  returns_mu |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mu   |
       _cons |   .0018803   .0024645     0.76   0.445      -.00295    .0067105
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .2639531   .0893526     2.95   0.003     .0888253    .4390809
             |
       _cons |   .0010906   .0001362     8.01   0.000     .0008236    .0013576
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    36.5982
 Prob > chi2(40)           =     0.6242

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    23.1768
 Prob > chi2(40)           =     0.9846

.           
.           eststo: arch returns_mu, arch(1) het(sanc)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  473.40246  
Iteration 1:   log likelihood =  473.98846  
Iteration 2:   log likelihood =  474.09697  
Iteration 3:   log likelihood =  474.10652  
Iteration 4:   log likelihood =  474.10783  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  474.10802  
Iteration 6:   log likelihood =  474.10806  

ARCH family regression -- multiplicative heteroskedasticity

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  474.1081                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
  returns_mu |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mu   |
       _cons |   .0015912   .0025106     0.63   0.526    -.0033294    .0065119
-------------+----------------------------------------------------------------
HET          |
        sanc |   .3112236   .2021953     1.54   0.124     -.085072    .7075192
       _cons |  -6.883703   .1339265   -51.40   0.000    -7.146195   -6.621212
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .2339098   .0805436     2.90   0.004     .0760472    .3917724
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    34.7485
 Prob > chi2(40)           =     0.7052

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    23.7428
 Prob > chi2(40)           =     0.9807

.           
.           eststo: arch returns_mu, arch(1) ar(14,17) het(sanc)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  477.25799  
Iteration 1:   log likelihood =   479.1128  
Iteration 2:   log likelihood =  481.40896  
Iteration 3:   log likelihood =  481.47985  
Iteration 4:   log likelihood =  481.49801  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  481.50128  
Iteration 6:   log likelihood =  481.50215  
Iteration 7:   log likelihood =  481.50215  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =      15.79
Log likelihood =  481.5022                        Prob > chi2     =     0.0004

------------------------------------------------------------------------------
             |                 OPG
  returns_mu |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mu   |
       _cons |   .0025248   .0035741     0.71   0.480    -.0044803    .0095299
-------------+----------------------------------------------------------------
ARMA         |
          ar |
        L14. |   .1565102   .0588402     2.66   0.008     .0411856    .2718349
        L17. |   .1777512    .057536     3.09   0.002     .0649827    .2905196
-------------+----------------------------------------------------------------
HET          |
        sanc |   .2578006   .2085455     1.24   0.216     -.150941    .6665422
       _cons |  -6.937405    .146797   -47.26   0.000    -7.225122   -6.649688
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .2407353   .0842144     2.86   0.004     .0756781    .4057925
------------------------------------------------------------------------------
(est7 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    16.9573
 Prob > chi2(40)           =     0.9995

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    30.0539
 Prob > chi2(40)           =     0.8737

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sanc Sanctions) nomtitles titl
> e(Micron Technology, Inc. 1997 (Japan)) nodep  

Micron Technology, Inc. 1997 (Japan)
-----------------------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)          (7)   
-----------------------------------------------------------------------------------------------------
returns_mu                                                                                           
Constant      -0.000       -0.000       -0.000       -0.000        0.002        0.002        0.003   
             (0.002)      (0.003)      (0.003)      (0.003)      (0.002)      (0.003)      (0.004)   
-----------------------------------------------------------------------------------------------------
SIGMA2                                                                                               
Constant       0.001***     0.001***     0.001***     0.001***                                       
             (0.000)      (0.000)      (0.000)      (0.000)                                          
-----------------------------------------------------------------------------------------------------
ARMA                                                                                                 
L.ar                        0.031                    -0.624                                          
                          (0.051)                   (0.713)                                          
L14.ar                                                                                       0.157***
                                                                                           (0.059)   
L17.ar                                                                                       0.178***
                                                                                           (0.058)   
L.ma                                     0.032        0.667                                          
                                       (0.051)      (0.685)                                          
-----------------------------------------------------------------------------------------------------
ARCH                                                                                                 
L.arch                                                             0.264***     0.234***     0.241***
                                                                 (0.089)      (0.081)      (0.084)   
Constant                                                           0.001***                          
                                                                 (0.000)                             
-----------------------------------------------------------------------------------------------------
HET                                                                                                  
Sanctions                                                                       0.311        0.258   
                                                                              (0.202)      (0.209)   
Constant                                                                       -6.884***    -6.937***
                                                                              (0.134)      (0.147)   
-----------------------------------------------------------------------------------------------------
N                253          253          253          253          253          253          253   
aic         -929.059     -927.301     -927.312     -925.829     -940.587     -940.216     -951.004   
bic         -921.992     -916.701     -916.712     -911.695     -929.986     -926.083     -929.804   
-----------------------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/mu1997.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant san
> c Sanctions) nomtitles title(Micron Technology, Inc. 1997 (Japan)) nodep replace         
(note: file rawtables/mu1997.tex not found)
(output written to rawtables/mu1997.tex)

. 
.   * Qualcomm 1997 (Japan)       
.   
.     * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen returns_qcom = ln(qcom_close/qcom_close[_n-1])              
(6,023 missing values generated)

. 
.         * Sanctions Variable    
.           gen sanc = 1 if date > td("25sep1997")
(7,486 missing values generated)

.           recode sanc(.=0)    
(sanc: 7486 changes made)

.           
.         * Limit Time Period
.           keep if date > td("31dec1996") & date < td("01jan1998")         
(12,841 observations deleted)

.           
.         * Set for analysis
.           tsset t
        time variable:  t, 7301 to 7553
                delta:  1 unit

.           
.         * Table A.33 Models
.           eststo clear 

.           
.           eststo: arch returns_qcom

(setting optimization to BHHH)
Iteration 0:   log likelihood =  494.81527  
Iteration 1:   log likelihood =  494.81527  

Time-series regression

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  494.8153                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
returns_qcom |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .0009338   .0021666     0.43   0.666    -.0033126    .0051803
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0011715   .0000773    15.15   0.000     .0010199    .0013231
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    35.3928
 Prob > chi2(40)           =     0.6776

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    26.1336
 Prob > chi2(40)           =     0.9555

.           
.           eststo: arch returns_qcom, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  494.39125  
Iteration 1:   log likelihood =  495.74063  
Iteration 2:   log likelihood =  495.88707  
Iteration 3:   log likelihood =  495.91124  
Iteration 4:   log likelihood =  495.91454  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  495.91507  
Iteration 6:   log likelihood =  495.91517  
Iteration 7:   log likelihood =  495.91517  

Time-series regression -- AR disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       2.51
Log likelihood =  495.9152                        Prob > chi2     =     0.1128

------------------------------------------------------------------------------
             |                 OPG
returns_qcom |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_qcom |
       _cons |   .0009126   .0024053     0.38   0.704    -.0038017    .0056269
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |    .093266   .0588132     1.59   0.113    -.0220057    .2085377
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0011614   .0000823    14.11   0.000         .001    .0013227
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    32.1120
 Prob > chi2(40)           =     0.8083

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    27.0854
 Prob > chi2(40)           =     0.9407

.           
.           eststo: arch returns_qcom, ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  494.33394  
Iteration 1:   log likelihood =  495.79832  
Iteration 2:   log likelihood =  495.94016  
Iteration 3:   log likelihood =  495.94898  
Iteration 4:   log likelihood =  495.94943  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  495.94946  

Time-series regression -- MA disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       2.61
Log likelihood =  495.9495                        Prob > chi2     =     0.1063

------------------------------------------------------------------------------
             |                 OPG
returns_qcom |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_qcom |
       _cons |   .0009115   .0023831     0.38   0.702    -.0037593    .0055823
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |   .0960796   .0594953     1.61   0.106     -.020529    .2126883
-------------+----------------------------------------------------------------
     /SIGMA2 |    .001161   .0000813    14.27   0.000     .0010016    .0013204
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    32.0040
 Prob > chi2(40)           =     0.8121

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    26.9793
 Prob > chi2(40)           =     0.9425

.           
.           eststo: arch returns_qcom, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  494.25982  
Iteration 1:   log likelihood =  495.53652  
Iteration 2:   log likelihood =  495.78696  
Iteration 3:   log likelihood =  495.94439  
Iteration 4:   log likelihood =  495.95467  
(switching optimization to BFGS)
Iteration 5:   log likelihood =   495.9579  
Iteration 6:   log likelihood =  495.95916  
Iteration 7:   log likelihood =  495.95926  
Iteration 8:   log likelihood =  495.95926  

Time-series regression -- ARMA disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =       2.68
Log likelihood =  495.9593                        Prob > chi2     =     0.2624

------------------------------------------------------------------------------
             |                 OPG
returns_qcom |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_qcom |
       _cons |    .000914   .0023754     0.38   0.700    -.0037417    .0055696
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.0745857   .6303567    -0.12   0.906    -1.310062    1.160891
             |
          ma |
         L1. |   .1698779   .6251456     0.27   0.786    -1.055385    1.395141
-------------+----------------------------------------------------------------
     /SIGMA2 |    .001161   .0000825    14.07   0.000     .0009993    .0013227
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    32.0132
 Prob > chi2(40)           =     0.8118

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    26.8779
 Prob > chi2(40)           =     0.9442

.           
.           eststo: arch returns_qcom, het(sanc)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  496.11371  
Iteration 1:   log likelihood =  500.21469  
Iteration 2:   log likelihood =  500.27246  
Iteration 3:   log likelihood =  500.27874  
Iteration 4:   log likelihood =  500.27886  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  500.27887  

Time-series regression -- multiplicative heteroskedasticity

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  500.2789                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
returns_qcom |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_qcom |
       _cons |   .0012597    .002078     0.61   0.544    -.0028131    .0053325
-------------+----------------------------------------------------------------
HET          |
        sanc |   .6381877   .1558233     4.10   0.000     .3327796    .9435958
       _cons |  -6.961636   .0909495   -76.54   0.000    -7.139893   -6.783378
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    35.4159
 Prob > chi2(40)           =     0.6766

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    31.5457
 Prob > chi2(40)           =     0.8278

.           
.           eststo: arch returns_qcom, arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  496.56799  
Iteration 1:   log likelihood =  498.14099  
Iteration 2:   log likelihood =  498.50903  
Iteration 3:   log likelihood =  498.57382  
Iteration 4:   log likelihood =  498.58877  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  498.59201  
Iteration 6:   log likelihood =  498.59288  
Iteration 7:   log likelihood =   498.5929  

ARCH family regression

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  498.5929                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
returns_qcom |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_qcom |
       _cons |   .0027009   .0022744     1.19   0.235    -.0017568    .0071586
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .2687923   .0786926     3.42   0.001     .1145575     .423027
             |
       _cons |    .000895   .0000956     9.36   0.000     .0007077    .0010824
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    36.2529
 Prob > chi2(40)           =     0.6397

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    27.1913
 Prob > chi2(40)           =     0.9388

.           
.           eststo: arch returns_qcom, arch(1) garch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  496.23454  
Iteration 1:   log likelihood =  497.47666  
Iteration 2:   log likelihood =  498.45121  
Iteration 3:   log likelihood =  498.85785  
Iteration 4:   log likelihood =   498.9801  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  499.01519  
Iteration 6:   log likelihood =  499.02525  
Iteration 7:   log likelihood =  499.02535  
Iteration 8:   log likelihood =  499.02535  

ARCH family regression

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  499.0254                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
returns_qcom |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_qcom |
       _cons |   .0029887   .0023142     1.29   0.197    -.0015471    .0075245
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .2279848   .0847156     2.69   0.007     .0619454    .3940243
             |
       garch |
         L1. |   .3258328   .3112076     1.05   0.295    -.2841228    .9357884
             |
       _cons |   .0005464   .0003133     1.74   0.081    -.0000675    .0011604
------------------------------------------------------------------------------
(est7 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    34.8078
 Prob > chi2(40)           =     0.7027

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    27.8291
 Prob > chi2(40)           =     0.9269

.           
.           eststo: arch returns_qcom, arch(1) het(sanc)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  500.11702  
Iteration 1:   log likelihood =  502.09526  
Iteration 2:   log likelihood =  502.14465  
Iteration 3:   log likelihood =  502.14894  
Iteration 4:   log likelihood =  502.14938  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  502.14941  
Iteration 6:   log likelihood =  502.14941  

ARCH family regression -- multiplicative heteroskedasticity

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  502.1494                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
returns_qcom |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_qcom |
       _cons |   .0024711   .0021777     1.13   0.256    -.0017971    .0067393
-------------+----------------------------------------------------------------
HET          |
        sanc |   .6347654   .1960151     3.24   0.001     .2505829    1.018948
       _cons |  -7.131445   .1151621   -61.93   0.000    -7.357159   -6.905732
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1690654   .0942645     1.79   0.073    -.0156897    .3538205
------------------------------------------------------------------------------
(est8 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    35.3514
 Prob > chi2(40)           =     0.6794

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    33.0337
 Prob > chi2(40)           =     0.7744

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sanc Sanctions) nomtitles titl
> e(Qualcomm 1997 (Japan)) nodep  

Qualcomm 1997 (Japan)
------------------------------------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)          (7)          (8)   
------------------------------------------------------------------------------------------------------------------
returns_~m                                                                                                        
Constant       0.001        0.001        0.001        0.001        0.001        0.003        0.003        0.002   
             (0.002)      (0.002)      (0.002)      (0.002)      (0.002)      (0.002)      (0.002)      (0.002)   
------------------------------------------------------------------------------------------------------------------
SIGMA2                                                                                                            
Constant       0.001***     0.001***     0.001***     0.001***                                                    
             (0.000)      (0.000)      (0.000)      (0.000)                                                       
------------------------------------------------------------------------------------------------------------------
ARMA                                                                                                              
L.ar                        0.093                    -0.075                                                       
                          (0.059)                   (0.630)                                                       
L.ma                                     0.096        0.170                                                       
                                       (0.059)      (0.625)                                                       
------------------------------------------------------------------------------------------------------------------
HET                                                                                                               
Sanctions                                                          0.638***                               0.635***
                                                                 (0.156)                                (0.196)   
Constant                                                          -6.962***                              -7.131***
                                                                 (0.091)                                (0.115)   
------------------------------------------------------------------------------------------------------------------
ARCH                                                                                                              
L.arch                                                                          0.269***     0.228***     0.169*  
                                                                              (0.079)      (0.085)      (0.094)   
L.garch                                                                                      0.326                
                                                                                           (0.311)                
Constant                                                                        0.001***     0.001*               
                                                                              (0.000)      (0.000)                
------------------------------------------------------------------------------------------------------------------
N                253          253          253          253          253          253          253          253   
aic         -985.631     -985.830     -985.899     -983.919     -994.558     -991.186     -990.051     -996.299   
bic         -978.564     -975.230     -975.299     -969.785     -983.958     -980.586     -975.917     -982.165   
------------------------------------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/qcom1997.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant s
> anc Sanctions) nomtitles title(Qualcomm 1997 (Japan)) nodep replace
(note: file rawtables/qcom1997.tex not found)
(output written to rawtables/qcom1997.tex)

.           
.   * Texas Instruments 1997 (Japan)
.   
.     * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen returns_txn  = ln(txn_close/txn_close[_n-1])                        
(3 missing values generated)

. 
.         * Sanctions Variable    
.           gen sanc = 1 if date > td("25sep1997")
(7,486 missing values generated)

.           recode sanc(.=0)    
(sanc: 7486 changes made)

.           
.         * Limit Time Period
.           keep if date > td("31dec1996") & date < td("01jan1998")         
(12,841 observations deleted)

.           
.         * Set for analysis
.           tsset t
        time variable:  t, 7301 to 7553
                delta:  1 unit

.           
.         * Table A.34 Models
.           eststo clear 

.           
.           eststo: arch returns_txn

(setting optimization to BHHH)
Iteration 0:   log likelihood =  655.72293  
Iteration 1:   log likelihood =  655.72293  

Time-series regression

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  655.7229                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 returns_txn |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .0009361   .0011432     0.82   0.413    -.0013045    .0031767
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0003283   .0000234    14.06   0.000     .0002826    .0003741
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    48.7908
 Prob > chi2(40)           =     0.1605

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.8618
 Prob > chi2(40)           =     0.4324

.           
.           eststo: arch returns_txn, ar(10)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  661.51474  
Iteration 1:   log likelihood =  661.94433  
Iteration 2:   log likelihood =  661.95184  
Iteration 3:   log likelihood =  661.95272  
Iteration 4:   log likelihood =  661.95285  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  661.95287  

Time-series regression -- AR disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       9.83
Log likelihood =  661.9529                        Prob > chi2     =     0.0017

------------------------------------------------------------------------------
             |                 OPG
 returns_txn |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_txn  |
       _cons |   .0010419   .0014133     0.74   0.461    -.0017282    .0038119
-------------+----------------------------------------------------------------
ARMA         |
          ar |
        L10. |   .2225991    .070997     3.14   0.002     .0834476    .3617506
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0003125   .0000213    14.67   0.000     .0002708    .0003543
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    34.3001
 Prob > chi2(40)           =     0.7240

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    36.9571
 Prob > chi2(40)           =     0.6080

.           
.           eststo: arch returns_txn, ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  654.71354  
Iteration 1:   log likelihood =  655.81681  
Iteration 2:   log likelihood =  655.83509  
Iteration 3:   log likelihood =  655.83632  
Iteration 4:   log likelihood =  655.83636  

Time-series regression -- MA disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       0.34
Log likelihood =  655.8364                        Prob > chi2     =     0.5577

------------------------------------------------------------------------------
             |                 OPG
 returns_txn |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_txn  |
       _cons |    .000937   .0011404     0.82   0.411    -.0012982    .0031722
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |  -.0322884   .0550784    -0.59   0.558    -.1402401    .0756633
-------------+----------------------------------------------------------------
     /SIGMA2 |    .000328   .0000236    13.89   0.000     .0002817    .0003743
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    48.4945
 Prob > chi2(40)           =     0.1677

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    41.2976
 Prob > chi2(40)           =     0.4137

.           
.           eststo: arch returns_txn, ar(10) het(sanc)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  656.33145  
Iteration 1:   log likelihood =  662.81363  
Iteration 2:   log likelihood =  663.00327  
Iteration 3:   log likelihood =  663.01627  
Iteration 4:   log likelihood =  663.01752  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  663.01765  
Iteration 6:   log likelihood =  663.01767  

Time-series regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       9.95
Log likelihood =  663.0177                        Prob > chi2     =     0.0016

------------------------------------------------------------------------------
             |                 OPG
 returns_txn |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_txn  |
       _cons |   .0010485   .0014147     0.74   0.459    -.0017242    .0038212
-------------+----------------------------------------------------------------
ARMA         |
          ar |
        L10. |   .2232459   .0707573     3.16   0.002     .0845641    .3619278
-------------+----------------------------------------------------------------
HET          |
        sanc |   .2877513   .1519398     1.89   0.058    -.0100452    .5855478
       _cons |  -8.155319   .0869048   -93.84   0.000     -8.32565   -7.984989
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.2356
 Prob > chi2(40)           =     0.5954

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.4912
 Prob > chi2(40)           =     0.4486

.           
.           eststo: arch returns_txn, ar(7,10) het(sanc)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  659.26635  
Iteration 1:   log likelihood =  664.79019  
Iteration 2:   log likelihood =  665.06408  
Iteration 3:   log likelihood =  665.08787  
Iteration 4:   log likelihood =  665.08996  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  665.09032  
Iteration 6:   log likelihood =   665.0904  
Iteration 7:   log likelihood =   665.0904  

Time-series regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =      15.31
Log likelihood =  665.0904                        Prob > chi2     =     0.0005

------------------------------------------------------------------------------
             |                 OPG
 returns_txn |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_txn  |
       _cons |   .0009939   .0012214     0.81   0.416       -.0014    .0033877
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L7. |   -.126669    .055768    -2.27   0.023    -.2359723   -.0173657
        L10. |   .2236525    .068856     3.25   0.001     .0886971    .3586078
-------------+----------------------------------------------------------------
HET          |
        sanc |   .3487421   .1559967     2.24   0.025     .0429942      .65449
       _cons |  -8.187889   .0882582   -92.77   0.000    -8.360872   -8.014906
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    30.5690
 Prob > chi2(40)           =     0.8588

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    42.2334
 Prob > chi2(40)           =     0.3747

.           
.           eststo: arch returns_txn, ar(7,10) arch(1) het(sanc)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  667.28419  
Iteration 1:   log likelihood =  668.68062  
Iteration 2:   log likelihood =  668.79676  
Iteration 3:   log likelihood =  668.82833  
Iteration 4:   log likelihood =  668.83522  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  668.83696  
Iteration 6:   log likelihood =  668.83752  
Iteration 7:   log likelihood =  668.83753  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =      14.90
Log likelihood =  668.8375                        Prob > chi2     =     0.0006

------------------------------------------------------------------------------
             |                 OPG
 returns_txn |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_txn  |
       _cons |   .0012997   .0011585     1.12   0.262    -.0009708    .0035703
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L7. |  -.1359748     .05539    -2.45   0.014    -.2445373   -.0274123
        L10. |   .2129988   .0670833     3.18   0.001      .081518    .3444795
-------------+----------------------------------------------------------------
HET          |
        sanc |   .2883518   .2427648     1.19   0.235    -.1874586    .7641621
       _cons |  -8.370556   .1165028   -71.85   0.000    -8.598897   -8.142215
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |    .180493   .0737112     2.45   0.014     .0360217    .3249643
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    30.3844
 Prob > chi2(40)           =     0.8642

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    46.4588
 Prob > chi2(40)           =     0.2236

.           
.           eststo: arch returns_txn, ar(7,10) arch(1) garch(1) het(sanc)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  667.99131  
Iteration 1:   log likelihood =  668.72726  
Iteration 2:   log likelihood =   668.7678  
Iteration 3:   log likelihood =  668.78769  
Iteration 4:   log likelihood =  668.82824  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  668.82906  
Iteration 6:   log likelihood =  668.83513  
Iteration 7:   log likelihood =   668.8376  
Iteration 8:   log likelihood =  668.83777  
Iteration 9:   log likelihood =  668.83779  
Iteration 10:  log likelihood =  668.83779  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =      14.72
Log likelihood =  668.8378                        Prob > chi2     =     0.0006

------------------------------------------------------------------------------
             |                 OPG
 returns_txn |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_txn  |
       _cons |      .0013   .0011787     1.10   0.270    -.0010102    .0036102
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L7. |  -.1358541   .0557325    -2.44   0.015    -.2450879   -.0266203
        L10. |   .2129527   .0671737     3.17   0.002     .0812946    .3446109
-------------+----------------------------------------------------------------
HET          |
        sanc |   .2877259   .2634137     1.09   0.275    -.2285555    .8040073
       _cons |  -8.375513   .4443732   -18.85   0.000    -9.246469   -7.504558
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1799687   .0824412     2.18   0.029      .018387    .3415505
             |
       garch |
         L1. |   .0046037   .4039281     0.01   0.991    -.7870808    .7962881
------------------------------------------------------------------------------
(est7 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    30.3810
 Prob > chi2(40)           =     0.8643

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    46.4057
 Prob > chi2(40)           =     0.2252

.           
.           eststo: arch returns_txn, ar(7,10) arch(1,2) garch(1,2) het(sanc)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  667.31131  
Iteration 1:   log likelihood =  668.17133  
Iteration 2:   log likelihood =  670.19875  
Iteration 3:   log likelihood =  670.55152  
Iteration 4:   log likelihood =   671.0119  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  671.39232  
Iteration 6:   log likelihood =  671.60873  
Iteration 7:   log likelihood =  671.89437  
Iteration 8:   log likelihood =  672.02379  
Iteration 9:   log likelihood =   672.1626  
Iteration 10:  log likelihood =   672.2472  
Iteration 11:  log likelihood =    672.264  
Iteration 12:  log likelihood =  672.27597  
Iteration 13:  log likelihood =  672.28328  
Iteration 14:  log likelihood =  672.28831  
(switching optimization to BHHH)
Iteration 15:  log likelihood =  672.32077  
Iteration 16:  log likelihood =   672.8515  
Iteration 17:  log likelihood =  673.60101  
Iteration 18:  log likelihood =  674.16927  
Iteration 19:  log likelihood =   674.2642  
(switching optimization to BFGS)
Iteration 20:  log likelihood =  674.34901  
Iteration 21:  log likelihood =  674.44328  
Iteration 22:  log likelihood =  674.49915  
Iteration 23:  log likelihood =  674.50343  
Iteration 24:  log likelihood =   674.5554  
Iteration 25:  log likelihood =  674.56343  
Iteration 26:  log likelihood =  674.56957  
Iteration 27:  log likelihood =  674.56975  
Iteration 28:  log likelihood =  674.57003  
Iteration 29:  log likelihood =  674.57006  
(switching optimization to BHHH)
Iteration 30:  log likelihood =  674.57007  
Iteration 31:  log likelihood =  674.57007  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =      21.24
Log likelihood =  674.5701                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
 returns_txn |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_txn  |
       _cons |   .0005544   .0010182     0.54   0.586    -.0014414    .0025501
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L7. |  -.1625166   .0548855    -2.96   0.003    -.2700902    -.054943
        L10. |   .2264684   .0686241     3.30   0.001     .0919677    .3609691
-------------+----------------------------------------------------------------
HET          |
        sanc |   .4333275   .1992937     2.17   0.030     .0427189    .8239361
       _cons |   -7.98304   .1631259   -48.94   0.000     -8.30276   -7.663319
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |    .081385    .025686     3.17   0.002     .0310413    .1317287
         L2. |  -.0329733   .0366011    -0.90   0.368    -.1047101    .0387635
             |
       garch |
         L1. |   .4907372   .1262541     3.89   0.000     .2432837    .7381908
         L2. |  -.8357472   .1021864    -8.18   0.000    -1.036029   -.6354656
------------------------------------------------------------------------------
(est8 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    33.5274
 Prob > chi2(40)           =     0.7553

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    33.4799
 Prob > chi2(40)           =     0.7571

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sanc Sanctions) nomtitles titl
> e(Texas Instruments 1997 (Japan)) nodep  

Texas Instruments 1997 (Japan)
------------------------------------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)          (7)          (8)   
------------------------------------------------------------------------------------------------------------------
returns_~n                                                                                                        
Constant       0.001        0.001        0.001        0.001        0.001        0.001        0.001        0.001   
             (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)   
------------------------------------------------------------------------------------------------------------------
SIGMA2                                                                                                            
Constant       0.000***     0.000***     0.000***                                                                 
             (0.000)      (0.000)      (0.000)                                                                    
------------------------------------------------------------------------------------------------------------------
ARMA                                                                                                              
L10.ar                      0.223***                  0.223***     0.224***     0.213***     0.213***     0.226***
                          (0.071)                   (0.071)      (0.069)      (0.067)      (0.067)      (0.069)   
L7.ar                                                             -0.127**     -0.136**     -0.136**     -0.163***
                                                                 (0.056)      (0.055)      (0.056)      (0.055)   
L.ma                                    -0.032                                                                    
                                       (0.055)                                                                    
------------------------------------------------------------------------------------------------------------------
HET                                                                                                               
Sanctions                                             0.288*       0.349**      0.288        0.288        0.433** 
                                                    (0.152)      (0.156)      (0.243)      (0.263)      (0.199)   
Constant                                             -8.155***    -8.188***    -8.371***    -8.376***    -7.983***
                                                    (0.087)      (0.088)      (0.117)      (0.444)      (0.163)   
------------------------------------------------------------------------------------------------------------------
ARCH                                                                                                              
L.arch                                                                          0.180**      0.180**      0.081***
                                                                              (0.074)      (0.082)      (0.026)   
L2.arch                                                                                                  -0.033   
                                                                                                        (0.037)   
L.garch                                                                                      0.005        0.491***
                                                                                           (0.404)      (0.126)   
L2.garch                                                                                                 -0.836***
                                                                                                        (0.102)   
------------------------------------------------------------------------------------------------------------------
N                253          253          253          253          253          253          253          253   
aic        -1307.446    -1317.906    -1305.673    -1318.035    -1320.181    -1325.675    -1323.676    -1331.140   
bic        -1300.379    -1307.306    -1295.073    -1303.902    -1302.514    -1304.475    -1298.942    -1299.340   
------------------------------------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/txn1997.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sa
> nc Sanctions) nomtitles title(Texas Instruments 1997 (Japan)) nodep replace  
(note: file rawtables/txn1997.tex not found)
(output written to rawtables/txn1997.tex)

. 
.   * Xilinix 1997 (Japan)
.   
.     * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen returns_xlnx = ln(xlnx_close/xlnx_close[_n-1])                      
(5,642 missing values generated)

. 
.         * Sanctions Variable    
.           gen sanc = 1 if date > td("25sep1997")
(7,486 missing values generated)

.           recode sanc(.=0)    
(sanc: 7486 changes made)

.           
.         * Limit Time Period
.           keep if date > td("31dec1996") & date < td("01jan1998")         
(12,841 observations deleted)

.           
.         * Set for analysis
.           tsset t
        time variable:  t, 7301 to 7553
                delta:  1 unit

.           
.         * Table A.35 Models
.           eststo clear 

.           
.           eststo: arch returns_xlnx

(setting optimization to BHHH)
Iteration 0:   log likelihood =   444.3456  
Iteration 1:   log likelihood =   444.3456  

Time-series regression

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  444.3456                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
returns_xlnx |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |  -.0001925   .0026273    -0.07   0.942    -.0053418    .0049568
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0017459   .0001037    16.84   0.000     .0015426    .0019491
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    35.7611
 Prob > chi2(40)           =     0.6615

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    85.9248
 Prob > chi2(40)           =     0.0000

.           
.           eststo: arch returns_xlnx, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =   445.1504  
Iteration 1:   log likelihood =  446.02025  
Iteration 2:   log likelihood =  446.11601  
Iteration 3:   log likelihood =  446.13006  
Iteration 4:   log likelihood =  446.13345  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  446.13407  
Iteration 6:   log likelihood =  446.13422  
Iteration 7:   log likelihood =  446.13423  

Time-series regression -- AR disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =       6.65
Log likelihood =  446.1342                        Prob > chi2     =     0.0099

------------------------------------------------------------------------------
             |                 OPG
returns_xlnx |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_xlnx |
       _cons |  -.0001736   .0023368    -0.07   0.941    -.0047536    .0044064
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.1185814   .0459846    -2.58   0.010    -.2087095   -.0284533
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0017214   .0001163    14.80   0.000     .0014934    .0019494
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    34.6398
 Prob > chi2(40)           =     0.7098

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    77.7013
 Prob > chi2(40)           =     0.0003

.           
.           eststo: arch returns_xlnx, ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  445.98648  
Iteration 1:   log likelihood =  446.80464  
Iteration 2:   log likelihood =  446.90512  
Iteration 3:   log likelihood =  446.91266  
Iteration 4:   log likelihood =  446.91459  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  446.91521  
Iteration 6:   log likelihood =  446.91544  
Iteration 7:   log likelihood =  446.91545  

Time-series regression -- MA disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(1)    =      15.82
Log likelihood =  446.9155                        Prob > chi2     =     0.0001

------------------------------------------------------------------------------
             |                 OPG
returns_xlnx |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_xlnx |
       _cons |  -.0001652   .0021548    -0.08   0.939    -.0043886    .0040581
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |  -.1725041   .0433774    -3.98   0.000    -.2575224   -.0874859
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0017108   .0001143    14.96   0.000     .0014867    .0019349
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    34.2609
 Prob > chi2(40)           =     0.7256

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    74.9391
 Prob > chi2(40)           =     0.0007

.           
.           eststo: arch returns_xlnx, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  446.83871  
Iteration 1:   log likelihood =  449.21227  
Iteration 2:   log likelihood =  449.66711  
Iteration 3:   log likelihood =  449.84134  
Iteration 4:   log likelihood =  449.88147  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  449.88797  
Iteration 6:   log likelihood =  449.89889  
Iteration 7:   log likelihood =  449.90042  
Iteration 8:   log likelihood =  449.90047  

Time-series regression -- ARMA disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =      78.49
Log likelihood =  449.9005                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
returns_xlnx |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_xlnx |
       _cons |  -.0002055   .0016881    -0.12   0.903    -.0035142    .0031031
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .5227974   .1927239     2.71   0.007     .1450655    .9005293
             |
          ma |
         L1. |  -.7034226   .1648153    -4.27   0.000    -1.026455   -.3803905
-------------+----------------------------------------------------------------
     /SIGMA2 |    .001671   .0001103    15.15   0.000     .0014549    .0018872
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    28.1628
 Prob > chi2(40)           =     0.9201

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    75.8903
 Prob > chi2(40)           =     0.0005

.           
.           eststo: arch returns_xlnx, ar(1) ma(1) arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  449.80013  
Iteration 1:   log likelihood =  454.23461  
Iteration 2:   log likelihood =  455.11689  
Iteration 3:   log likelihood =  455.23422  
Iteration 4:   log likelihood =  455.26111  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  455.26649  
Iteration 6:   log likelihood =  455.27647  
Iteration 7:   log likelihood =  455.27717  
Iteration 8:   log likelihood =  455.27719  

ARCH family regression -- ARMA disturbances

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =      45.44
Log likelihood =  455.2772                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
returns_xlnx |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_xlnx |
       _cons |  -.0002635   .0017034    -0.15   0.877    -.0036021    .0030752
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .5827261   .2045594     2.85   0.004      .181797    .9836551
             |
          ma |
         L1. |  -.7328989   .1677918    -4.37   0.000    -1.061765    -.404033
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1219146   .0617338     1.97   0.048     .0009185    .2429107
             |
       _cons |   .0014285   .0001116    12.80   0.000     .0012099    .0016472
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    26.6317
 Prob > chi2(40)           =     0.9481

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.6231
 Prob > chi2(40)           =     0.4428

.           
.           eststo: arch returns_xlnx, ar(1) ma(1) arch(1) het(sanc)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  459.24066  
Iteration 1:   log likelihood =  462.43577  
Iteration 2:   log likelihood =  463.18009  
Iteration 3:   log likelihood =  463.29577  
Iteration 4:   log likelihood =  463.30839  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  463.31061  
Iteration 6:   log likelihood =  463.31119  
Iteration 7:   log likelihood =  463.31123  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 7301 - 7553                               Number of obs   =        253
Distribution: Gaussian                            Wald chi2(2)    =      71.38
Log likelihood =  463.3112                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
returns_xlnx |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_xlnx |
       _cons |   .0004953   .0013883     0.36   0.721    -.0022257    .0032164
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .6437671   .1646786     3.91   0.000     .3210029    .9665312
             |
          ma |
         L1. |  -.7919894    .130727    -6.06   0.000    -1.048209   -.5357692
-------------+----------------------------------------------------------------
HET          |
        sanc |   .8556532   .1752193     4.88   0.000     .5122297    1.199077
       _cons |  -6.809386   .1093463   -62.27   0.000    -7.023701   -6.595071
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .0855008   .0696779     1.23   0.220    -.0510653     .222067
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    30.8912
 Prob > chi2(40)           =     0.8489

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    24.3413
 Prob > chi2(40)           =     0.9758

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sanc Sanctions) nomtitles titl
> e(Xilinix 1997 (Japan)) nodep  

Xilinix 1997 (Japan)
----------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)   
----------------------------------------------------------------------------------------
returns_~x                                                                              
Constant      -0.000       -0.000       -0.000       -0.000       -0.000        0.000   
             (0.003)      (0.002)      (0.002)      (0.002)      (0.002)      (0.001)   
----------------------------------------------------------------------------------------
SIGMA2                                                                                  
Constant       0.002***     0.002***     0.002***     0.002***                          
             (0.000)      (0.000)      (0.000)      (0.000)                             
----------------------------------------------------------------------------------------
ARMA                                                                                    
L.ar                       -0.119***                  0.523***     0.583***     0.644***
                          (0.046)                   (0.193)      (0.205)      (0.165)   
L.ma                                    -0.173***    -0.703***    -0.733***    -0.792***
                                       (0.043)      (0.165)      (0.168)      (0.131)   
----------------------------------------------------------------------------------------
ARCH                                                                                    
L.arch                                                             0.122**      0.086   
                                                                 (0.062)      (0.070)   
Constant                                                           0.001***             
                                                                 (0.000)                
----------------------------------------------------------------------------------------
HET                                                                                     
Sanctions                                                                       0.856***
                                                                              (0.175)   
Constant                                                                       -6.809***
                                                                              (0.109)   
----------------------------------------------------------------------------------------
N                253          253          253          253          253          253   
aic         -884.691     -886.268     -887.831     -891.801     -900.554     -914.622   
bic         -877.624     -875.668     -877.231     -877.667     -882.887     -893.422   
----------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/xlnx1997.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant s
> anc Sanctions) nomtitles title(Xilinix 1997 (Japan)) nodep replace 
(note: file rawtables/xlnx1997.tex not found)
(output written to rawtables/xlnx1997.tex)

. 
.   * Allergan 1999 (Spain)
.   
.     * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen returns_agn = ln(agn_close/agn_close[_n-1])                         
(6,321 missing values generated)

. 
.         * Sanctions Variable    
.           gen sanct = 1 if date > td("05apr1999") & date < td("11may1999")
(13,069 missing values generated)

.           recode sanct(.=0)
(sanct: 13069 changes made)

.           
.         * Limit Time Period
.           keep if date > td("31dec1998") & date < td("01jan2000")         
(12,842 observations deleted)

.           
.         * Set for analysis
.           tsset t
        time variable:  t, 7806 to 8057
                delta:  1 unit

.           
.         * Table A.36 Models
.           eststo clear 

.           
.           eststo: arch returns_agn

(setting optimization to BHHH)
Iteration 0:   log likelihood =  515.51682  
Iteration 1:   log likelihood =  515.51682  

Time-series regression

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  515.5168                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 returns_agn |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |  -.0022335   .0019708    -1.13   0.257    -.0060962    .0016292
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0009787   .0000742    13.19   0.000     .0008333    .0011241
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    26.9016
 Prob > chi2(40)           =     0.9438

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    29.7217
 Prob > chi2(40)           =     0.8828

.           
.           eststo: arch returns_agn, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  515.99195  
Iteration 1:   log likelihood =  516.66669  
Iteration 2:   log likelihood =  516.67409  
Iteration 3:   log likelihood =   516.6744  
Iteration 4:   log likelihood =  516.67441  

Time-series regression -- AR disturbances

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       2.28
Log likelihood =  516.6744                        Prob > chi2     =     0.1312

------------------------------------------------------------------------------
             |                 OPG
 returns_agn |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_agn  |
       _cons |  -.0022105   .0018029    -1.23   0.220    -.0057442    .0013231
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   -.095664   .0633833    -1.51   0.131    -.2198929     .028565
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0009698   .0000735    13.19   0.000     .0008258    .0011139
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    25.3210
 Prob > chi2(40)           =     0.9658

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    29.4347
 Prob > chi2(40)           =     0.8904

.           
.           eststo: arch returns_agn, ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  515.71441  
Iteration 1:   log likelihood =   516.6743  
Iteration 2:   log likelihood =  516.71304  
Iteration 3:   log likelihood =  516.71498  
Iteration 4:   log likelihood =  516.71508  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  516.71508  

Time-series regression -- MA disturbances

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       2.52
Log likelihood =  516.7151                        Prob > chi2     =     0.1123

------------------------------------------------------------------------------
             |                 OPG
 returns_agn |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_agn  |
       _cons |  -.0022066   .0017832    -1.24   0.216    -.0057017    .0012884
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |   -.098796   .0622205    -1.59   0.112     -.220746     .023154
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0009694   .0000737    13.16   0.000     .0008251    .0011138
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    25.1966
 Prob > chi2(40)           =     0.9673

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    29.3356
 Prob > chi2(40)           =     0.8929

.           
.           eststo: arch returns_agn, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  515.56608  
Iteration 1:   log likelihood =  516.68999  
Iteration 2:   log likelihood =   516.7209  
Iteration 3:   log likelihood =  516.72357  
Iteration 4:   log likelihood =  516.72412  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  516.72452  
Iteration 6:   log likelihood =  516.72512  
Iteration 7:   log likelihood =  516.72512  

Time-series regression -- ARMA disturbances

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =       2.65
Log likelihood =  516.7251                        Prob > chi2     =     0.2654

------------------------------------------------------------------------------
             |                 OPG
 returns_agn |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_agn  |
       _cons |  -.0022057   .0017755    -1.24   0.214    -.0056856    .0012743
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0765971   .7233379     0.11   0.916    -1.341119    1.494313
             |
          ma |
         L1. |   -.174486   .7097339    -0.25   0.806    -1.565539    1.216567
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0009693   .0000745    13.00   0.000     .0008232    .0011155
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    25.1176
 Prob > chi2(40)           =     0.9681

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    29.2258
 Prob > chi2(40)           =     0.8957

.           
.           eststo: arch returns_agn, het(sanct)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  510.79625  
Iteration 1:   log likelihood =    516.781  
Iteration 2:   log likelihood =  517.14432  
Iteration 3:   log likelihood =  517.17403  
Iteration 4:   log likelihood =  517.17845  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  517.17877  
Iteration 6:   log likelihood =   517.1788  
Iteration 7:   log likelihood =   517.1788  

Time-series regression -- multiplicative heteroskedasticity

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  517.1788                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 returns_agn |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_agn  |
       _cons |  -.0021983   .0019625    -1.12   0.263    -.0060447    .0016481
-------------+----------------------------------------------------------------
HET          |
       sanct |   .5077185   .3246555     1.56   0.118    -.1285946    1.144032
       _cons |  -6.992836   .0776026   -90.11   0.000    -7.144934   -6.840737
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    28.4141
 Prob > chi2(40)           =     0.9147

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    28.5847
 Prob > chi2(40)           =     0.9109

.           
.           eststo: arch returns_agn, ma(1) het(sanct)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  514.90888  
Iteration 1:   log likelihood =  517.95032  
Iteration 2:   log likelihood =   518.1069  
Iteration 3:   log likelihood =  518.11082  
Iteration 4:   log likelihood =  518.11107  
(switching optimization to BFGS)
Iteration 5:   log likelihood =   518.1111  
Iteration 6:   log likelihood =   518.1111  

Time-series regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       1.96
Log likelihood =  518.1111                        Prob > chi2     =     0.1613

------------------------------------------------------------------------------
             |                 OPG
 returns_agn |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_agn  |
       _cons |  -.0021718   .0017948    -1.21   0.226    -.0056895    .0013458
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |  -.0865745   .0618136    -1.40   0.161    -.2077269    .0345779
-------------+----------------------------------------------------------------
HET          |
       sanct |   .4707583   .3181548     1.48   0.139    -.1528136     1.09433
       _cons |  -6.996575   .0779141   -89.80   0.000    -7.149284   -6.843866
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    26.7995
 Prob > chi2(40)           =     0.9454

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    27.0473
 Prob > chi2(40)           =     0.9413

.   
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sanc Sanctions) nomtitles titl
> e(Allergan 1999 (Spain)) nodep  

Allergan 1999 (Spain)
----------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)   
----------------------------------------------------------------------------------------
returns_~n                                                                              
Constant      -0.002       -0.002       -0.002       -0.002       -0.002       -0.002   
             (0.002)      (0.002)      (0.002)      (0.002)      (0.002)      (0.002)   
----------------------------------------------------------------------------------------
SIGMA2                                                                                  
Constant       0.001***     0.001***     0.001***     0.001***                          
             (0.000)      (0.000)      (0.000)      (0.000)                             
----------------------------------------------------------------------------------------
ARMA                                                                                    
L.ar                       -0.096                     0.077                             
                          (0.063)                   (0.723)                             
L.ma                                    -0.099       -0.174                    -0.087   
                                       (0.062)      (0.710)                   (0.062)   
----------------------------------------------------------------------------------------
HET                                                                                     
sanct                                                              0.508        0.471   
                                                                 (0.325)      (0.318)   
Constant                                                          -6.993***    -6.997***
                                                                 (0.078)      (0.078)   
----------------------------------------------------------------------------------------
N                252          252          252          252          252          252   
aic        -1027.034    -1027.349    -1027.430    -1025.450    -1028.358    -1028.222   
bic        -1019.975    -1016.761    -1016.842    -1011.333    -1017.769    -1014.104   
----------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/agn1999.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sa
> nc Sanctions) nomtitles title(Allergan 1999 (Spain)) nodep replace 
(note: file rawtables/agn1999.tex not found)
(output written to rawtables/agn1999.tex)

.     
.   * Johnson & Johnson 1999 (Spain)
.       
.         * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen returns_jnj = ln(jnj_close/jnj_close[_n-1])                         
(2,247 missing values generated)

. 
.         * Sanctions Variable    
.           gen sanct = 1 if date > td("05apr1999") & date < td("11may1999")
(13,069 missing values generated)

.           recode sanct(.=0)
(sanct: 13069 changes made)

.           
.         * Limit Time Period
.           keep if date > td("31dec1998") & date < td("01jan2000")         
(12,842 observations deleted)

.           
.         * Set for analysis
.           tsset t
        time variable:  t, 7806 to 8057
                delta:  1 unit

.           
.         * Table A.37 Models
.           eststo clear 

.           
.           eststo: arch returns_jnj

(setting optimization to BHHH)
Iteration 0:   log likelihood =  663.22514  
Iteration 1:   log likelihood =  663.22514  

Time-series regression

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  663.2251                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 returns_jnj |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .0004205   .0011459     0.37   0.714    -.0018254    .0026664
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0003031   .0000249    12.17   0.000     .0002543    .0003519
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    38.6672
 Prob > chi2(40)           =     0.5302

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    29.3114
 Prob > chi2(40)           =     0.8935

.           
.           eststo: arch returns_jnj, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  665.53277  
Iteration 1:   log likelihood =  666.36733  
Iteration 2:   log likelihood =  666.47222  
Iteration 3:   log likelihood =  666.48724  
Iteration 4:   log likelihood =   666.4892  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  666.48946  
Iteration 6:   log likelihood =   666.4895  

Time-series regression -- AR disturbances

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       7.87
Log likelihood =  666.4895                        Prob > chi2     =     0.0050

------------------------------------------------------------------------------
             |                 OPG
 returns_jnj |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_jnj  |
       _cons |   .0004053   .0013512     0.30   0.764     -.002243    .0030536
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .1599309   .0570088     2.81   0.005     .0481958    .2716661
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0002953   .0000239    12.38   0.000     .0002486    .0003421
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    32.4316
 Prob > chi2(40)           =     0.7969

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    25.3938
 Prob > chi2(40)           =     0.9650

.           
.           eststo: arch returns_jnj, ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  666.67128  
Iteration 1:   log likelihood =    667.402  
Iteration 2:   log likelihood =  667.49372  
Iteration 3:   log likelihood =  667.50804  
Iteration 4:   log likelihood =  667.51041  
(switching optimization to BFGS)
Iteration 5:   log likelihood =   667.5108  
Iteration 6:   log likelihood =  667.51088  
Iteration 7:   log likelihood =  667.51088  

Time-series regression -- MA disturbances

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =      13.63
Log likelihood =  667.5109                        Prob > chi2     =     0.0002

------------------------------------------------------------------------------
             |                 OPG
 returns_jnj |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_jnj  |
       _cons |   .0004003   .0013599     0.29   0.768     -.002265    .0030656
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |   .2037587   .0551864     3.69   0.000     .0955953     .311922
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0002929   .0000235    12.46   0.000     .0002468     .000339
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    30.8415
 Prob > chi2(40)           =     0.8505

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    24.7352
 Prob > chi2(40)           =     0.9721

.           
.           eststo: arch returns_jnj, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  666.55367  
Iteration 1:   log likelihood =  667.79043  
Iteration 2:   log likelihood =  668.00609  
Iteration 3:   log likelihood =  668.03562  
Iteration 4:   log likelihood =  668.03787  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  668.03804  
Iteration 6:   log likelihood =  668.03805  

Time-series regression -- ARMA disturbances

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =      18.91
Log likelihood =   668.038                        Prob > chi2     =     0.0001

------------------------------------------------------------------------------
             |                 OPG
 returns_jnj |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_jnj  |
       _cons |   .0004046   .0012946     0.31   0.755    -.0021327     .002942
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.2721976   .2880179    -0.95   0.345    -.8367023    .2923072
             |
          ma |
         L1. |   .4622494   .2670976     1.73   0.084    -.0612524    .9857511
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0002917   .0000233    12.50   0.000      .000246    .0003375
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    30.9927
 Prob > chi2(40)           =     0.8457

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    25.0466
 Prob > chi2(40)           =     0.9689

.           
.           eststo: arch returns_jnj, het(sanct)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  667.79481  
Iteration 1:   log likelihood =  668.30299  
Iteration 2:   log likelihood =  668.66561  
Iteration 3:   log likelihood =  668.68007  
Iteration 4:   log likelihood =  668.68137  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  668.68147  
Iteration 6:   log likelihood =  668.68148  

Time-series regression -- multiplicative heteroskedasticity

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  668.6815                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 returns_jnj |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_jnj  |
       _cons |   .0004853   .0010961     0.44   0.658     -.001663    .0026336
-------------+----------------------------------------------------------------
HET          |
       sanct |   .8765396   .3016292     2.91   0.004     .2853571    1.467722
       _cons |  -8.231862   .0907972   -90.66   0.000    -8.409821   -8.053902
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    38.6977
 Prob > chi2(40)           =     0.5289

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    26.9376
 Prob > chi2(40)           =     0.9432

.           
.           eststo: arch returns_jnj, ma(1) het(sanct)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  670.14384  
Iteration 1:   log likelihood =  672.60325  
Iteration 2:   log likelihood =  673.07758  
Iteration 3:   log likelihood =  673.18537  
Iteration 4:   log likelihood =  673.19688  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  673.19816  
Iteration 6:   log likelihood =  673.19829  
Iteration 7:   log likelihood =  673.19829  

Time-series regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       9.74
Log likelihood =  673.1983                        Prob > chi2     =     0.0018

------------------------------------------------------------------------------
             |                 OPG
 returns_jnj |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_jnj  |
       _cons |   .0004801   .0013011     0.37   0.712      -.00207    .0030303
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |    .208933   .0669446     3.12   0.002     .0777239     .340142
-------------+----------------------------------------------------------------
HET          |
       sanct |   .8932175   .2734237     3.27   0.001     .3573169    1.429118
       _cons |  -8.269335   .0931473   -88.78   0.000    -8.451901    -8.08677
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    30.0034
 Prob > chi2(40)           =     0.8751

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    26.1906
 Prob > chi2(40)           =     0.9547

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sanc Sanctions) nomtitles titl
> e(Johnson & Johnson 1999 (Spain)) nodep  

Johnson & Johnson 1999 (Spain)
----------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)   
----------------------------------------------------------------------------------------
returns_~j                                                                              
Constant       0.000        0.000        0.000        0.000        0.000        0.000   
             (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)   
----------------------------------------------------------------------------------------
SIGMA2                                                                                  
Constant       0.000***     0.000***     0.000***     0.000***                          
             (0.000)      (0.000)      (0.000)      (0.000)                             
----------------------------------------------------------------------------------------
ARMA                                                                                    
L.ar                        0.160***                 -0.272                             
                          (0.057)                   (0.288)                             
L.ma                                     0.204***     0.462*                    0.209***
                                       (0.055)      (0.267)                   (0.067)   
----------------------------------------------------------------------------------------
HET                                                                                     
sanct                                                              0.877***     0.893***
                                                                 (0.302)      (0.273)   
Constant                                                          -8.232***    -8.269***
                                                                 (0.091)      (0.093)   
----------------------------------------------------------------------------------------
N                252          252          252          252          252          252   
aic        -1322.450    -1326.979    -1329.022    -1328.076    -1331.363    -1338.397   
bic        -1315.391    -1316.391    -1318.433    -1313.958    -1320.775    -1324.279   
----------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/jnj1999.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sa
> nc Sanctions) nomtitles title(Johnson & Johnson 1999 (Spain)) nodep replace 
(note: file rawtables/jnj1999.tex not found)
(output written to rawtables/jnj1999.tex)

. 
.   * Eli Lilly 1999 (Spain)
.   
.         * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen returns_lly = ln(lly_close/lly_close[_n-1])                 
(3,150 missing values generated)

. 
.         * Sanctions Variable    
.           gen sanct = 1 if date > td("05apr1999") & date < td("11may1999")
(13,069 missing values generated)

.           recode sanct(.=0)
(sanct: 13069 changes made)

.           
.         * Limit Time Period
.           keep if date > td("31dec1998") & date < td("01jan2000")         
(12,842 observations deleted)

.           
.         * Set for analysis
.           tsset t
        time variable:  t, 7806 to 8057
                delta:  1 unit

.           
.         * Table A.38 Models
.           eststo clear 

.           
.           eststo: arch returns_lly

(setting optimization to BHHH)
Iteration 0:   log likelihood =  567.21114  
Iteration 1:   log likelihood =  567.21114  

Time-series regression

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  567.2111                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 returns_lly |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |  -.0011509   .0016094    -0.72   0.475    -.0043052    .0020034
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0006493   .0000449    14.47   0.000     .0005614    .0007373
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    47.3222
 Prob > chi2(40)           =     0.1985

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    19.4953
 Prob > chi2(40)           =     0.9974

.           
.           eststo: arch returns_lly, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  566.33461  
Iteration 1:   log likelihood =  567.86827  
Iteration 2:   log likelihood =  567.93903  
Iteration 3:   log likelihood =  567.94098  
Iteration 4:   log likelihood =  567.94105  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  567.94105  

Time-series regression -- AR disturbances

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       1.60
Log likelihood =   567.941                        Prob > chi2     =     0.2054

------------------------------------------------------------------------------
             |                 OPG
 returns_lly |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_lly  |
       _cons |  -.0011653   .0017746    -0.66   0.511    -.0046435     .002313
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0760068   .0600214     1.27   0.205    -.0416329    .1936466
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0006456   .0000444    14.54   0.000     .0005585    .0007326
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    47.1583
 Prob > chi2(40)           =     0.2031

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    19.6123
 Prob > chi2(40)           =     0.9972

.           
.           eststo: arch returns_lly, ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  566.10496  
Iteration 1:   log likelihood =   567.9208  
Iteration 2:   log likelihood =  567.99953  
Iteration 3:   log likelihood =  568.01051  
Iteration 4:   log likelihood =   568.0117  
(switching optimization to BFGS)
Iteration 5:   log likelihood =   568.0118  
Iteration 6:   log likelihood =  568.01182  

Time-series regression -- MA disturbances

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       2.01
Log likelihood =  568.0118                        Prob > chi2     =     0.1565

------------------------------------------------------------------------------
             |                 OPG
 returns_lly |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_lly  |
       _cons |   -.001166    .001779    -0.66   0.512    -.0046527    .0023208
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |   .0832759   .0587721     1.42   0.157    -.0319153    .1984671
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0006452   .0000444    14.54   0.000     .0005582    .0007322
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    46.7858
 Prob > chi2(40)           =     0.2138

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    19.6742
 Prob > chi2(40)           =     0.9971

.           
.           eststo: arch returns_lly, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  566.05142  
Iteration 1:   log likelihood =  566.83495  
Iteration 2:   log likelihood =  567.48537  
Iteration 3:   log likelihood =  568.08167  
Iteration 4:   log likelihood =  568.09378  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  568.09945  
Iteration 6:   log likelihood =  568.10374  
Iteration 7:   log likelihood =  568.10381  
Iteration 8:   log likelihood =  568.10382  

Time-series regression -- ARMA disturbances

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =       2.53
Log likelihood =  568.1038                        Prob > chi2     =     0.2829

------------------------------------------------------------------------------
             |                 OPG
 returns_lly |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_lly  |
       _cons |   -.001163   .0017557    -0.66   0.508     -.004604     .002278
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.1939281   .7606799    -0.25   0.799    -1.684833    1.296977
             |
          ma |
         L1. |   .2766901   .7430749     0.37   0.710     -1.17971     1.73309
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0006448   .0000443    14.54   0.000     .0005579    .0007317
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    45.6262
 Prob > chi2(40)           =     0.2497

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    19.8310
 Prob > chi2(40)           =     0.9968

.           
.           eststo: arch returns_lly, het(sanct)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  569.43786  
Iteration 1:   log likelihood =  569.62882  
Iteration 2:   log likelihood =  569.70168  
Iteration 3:   log likelihood =  569.71042  
Iteration 4:   log likelihood =  569.71426  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  569.71567  
Iteration 6:   log likelihood =  569.71658  
Iteration 7:   log likelihood =  569.71659  

Time-series regression -- multiplicative heteroskedasticity

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  569.7166                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 returns_lly |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_lly  |
       _cons |  -.0008952   .0016406    -0.55   0.585    -.0041107    .0023203
-------------+----------------------------------------------------------------
HET          |
       sanct |   .6159843   .1917318     3.21   0.001     .2401969    .9917717
       _cons |  -7.420545   .0993392   -74.70   0.000    -7.615247   -7.225844
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    43.4968
 Prob > chi2(40)           =     0.3248

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    36.8301
 Prob > chi2(40)           =     0.6137

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sanc Sanctions) nomtitles titl
> e(Eli Lilly 1999 (Spain)) nodep  

Eli Lilly 1999 (Spain)
---------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)   
---------------------------------------------------------------------------
returns_~y                                                                 
Constant      -0.001       -0.001       -0.001       -0.001       -0.001   
             (0.002)      (0.002)      (0.002)      (0.002)      (0.002)   
---------------------------------------------------------------------------
SIGMA2                                                                     
Constant       0.001***     0.001***     0.001***     0.001***             
             (0.000)      (0.000)      (0.000)      (0.000)                
---------------------------------------------------------------------------
ARMA                                                                       
L.ar                        0.076                    -0.194                
                          (0.060)                   (0.761)                
L.ma                                     0.083        0.277                
                                       (0.059)      (0.743)                
---------------------------------------------------------------------------
HET                                                                        
sanct                                                              0.616***
                                                                 (0.192)   
Constant                                                          -7.421***
                                                                 (0.099)   
---------------------------------------------------------------------------
N                252          252          252          252          252   
aic        -1130.422    -1129.882    -1130.024    -1128.208    -1133.433   
bic        -1123.363    -1119.294    -1119.435    -1114.090    -1122.845   
---------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/lly1999.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sa
> nc Sanctions) nomtitles title(Eli Lilly 1999 (Spain)) nodep replace 
(note: file rawtables/lly1999.tex not found)
(output written to rawtables/lly1999.tex)

. 
.   * Merck & Co. 1999 (Spain)
.   
.     * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen returns_mrk = ln(mrk_close/mrk_close[_n-1])                 
(3,148 missing values generated)

. 
.         * Sanctions Variable    
.           gen sanct = 1 if date > td("05apr1999") & date < td("11may1999")
(13,069 missing values generated)

.           recode sanct(.=0)
(sanct: 13069 changes made)

.           
.         * Limit Time Period
.           keep if date > td("31dec1998") & date < td("01jan2000")         
(12,842 observations deleted)

.           
.         * Set for analysis
.           tsset t
        time variable:  t, 7806 to 8057
                delta:  1 unit

.           
.         * Table A.39 Models
.           eststo clear    

.           
.           eststo: arch returns_mrk

(setting optimization to BHHH)
Iteration 0:   log likelihood =  632.33956  
Iteration 1:   log likelihood =  632.33956  

Time-series regression

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  632.3396                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 returns_mrk |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |  -.0003698    .001249    -0.30   0.767    -.0028179    .0020782
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0003872   .0000364    10.64   0.000     .0003159    .0004586
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    39.7020
 Prob > chi2(40)           =     0.4835

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    48.9311
 Prob > chi2(40)           =     0.1572

.           
.           eststo: arch returns_mrk, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  632.14053  
Iteration 1:   log likelihood =  632.91339  
Iteration 2:   log likelihood =  632.93236  
Iteration 3:   log likelihood =  632.93342  
Iteration 4:   log likelihood =   632.9335  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  632.93351  

Time-series regression -- AR disturbances

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       1.03
Log likelihood =  632.9335                        Prob > chi2     =     0.3099

------------------------------------------------------------------------------
             |                 OPG
 returns_mrk |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mrk  |
       _cons |   -.000367   .0013384    -0.27   0.784    -.0029902    .0022563
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0685641   .0675219     1.02   0.310    -.0637764    .2009046
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0003855   .0000366    10.52   0.000     .0003136    .0004573
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.8788
 Prob > chi2(40)           =     0.5661

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    49.3194
 Prob > chi2(40)           =     0.1483

.           
.           eststo: arch returns_mrk, ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  632.14865  
Iteration 1:   log likelihood =  632.92214  
Iteration 2:   log likelihood =  632.94242  
Iteration 3:   log likelihood =  632.94252  
Iteration 4:   log likelihood =  632.94253  

Time-series regression -- MA disturbances

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       1.13
Log likelihood =  632.9425                        Prob > chi2     =     0.2870

------------------------------------------------------------------------------
             |                 OPG
 returns_mrk |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mrk  |
       _cons |  -.0003683   .0013333    -0.28   0.782    -.0029814    .0022449
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |   .0696378   .0654046     1.06   0.287    -.0585528    .1978284
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0003854   .0000367    10.51   0.000     .0003135    .0004573
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.8085
 Prob > chi2(40)           =     0.5693

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    49.3056
 Prob > chi2(40)           =     0.1486

.           
.           eststo: arch returns_mrk, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =   632.1238  
Iteration 1:   log likelihood =  632.91783  
Iteration 2:   log likelihood =  632.94328  
Iteration 3:   log likelihood =  632.94512  
Iteration 4:   log likelihood =  632.94527  
(switching optimization to BFGS)
BFGS stepping has contracted, resetting BFGS Hessian (0)
Iteration 5:   log likelihood =  632.94529  
Iteration 6:   log likelihood =  632.94529  (backed up)
Iteration 7:   log likelihood =  632.94529  (backed up)
Iteration 8:   log likelihood =  632.94529  

Time-series regression -- ARMA disturbances

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =       1.24
Log likelihood =  632.9453                        Prob > chi2     =     0.5372

------------------------------------------------------------------------------
             |                 OPG
 returns_mrk |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mrk  |
       _cons |  -.0003679   .0013272    -0.28   0.782    -.0029693    .0022334
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.0705753   .9300103    -0.08   0.940    -1.893362    1.752211
             |
          ma |
         L1. |   .1399256    .909038     0.15   0.878    -1.641756    1.921607
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0003854   .0000368    10.48   0.000     .0003133    .0004574
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.7419
 Prob > chi2(40)           =     0.5724

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    49.2591
 Prob > chi2(40)           =     0.1497

.           
.           eststo: arch returns_mrk, arch(3)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  632.82848  
Iteration 1:   log likelihood =  633.26218  
Iteration 2:   log likelihood =  633.27938  
Iteration 3:   log likelihood =  633.27976  
Iteration 4:   log likelihood =  633.27977  

ARCH family regression

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  633.2798                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 returns_mrk |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mrk  |
       _cons |  -.0001506   .0012378    -0.12   0.903    -.0025767    .0022754
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L3. |   .0728301   .0691818     1.05   0.292    -.0627637     .208424
             |
       _cons |   .0003577   .0000414     8.65   0.000     .0002766    .0004387
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    39.3338
 Prob > chi2(40)           =     0.5001

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    42.8449
 Prob > chi2(40)           =     0.3501

.           
.           eststo: arch returns_mrk, het(sanct)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  633.85803  
Iteration 1:   log likelihood =  634.49114  
Iteration 2:   log likelihood =  634.58009  
Iteration 3:   log likelihood =  634.58264  
Iteration 4:   log likelihood =  634.58275  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  634.58276  
Iteration 6:   log likelihood =  634.58276  

Time-series regression -- multiplicative heteroskedasticity

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  634.5828                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 returns_mrk |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mrk  |
       _cons |  -.0002385   .0012362    -0.19   0.847    -.0026614    .0021844
-------------+----------------------------------------------------------------
HET          |
       sanct |   .5845568   .3094638     1.89   0.059     -.021981    1.191095
       _cons |   -7.93224   .1044988   -75.91   0.000    -8.137054   -7.727426
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    36.4491
 Prob > chi2(40)           =     0.6309

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    45.6173
 Prob > chi2(40)           =     0.2500

.           
.           eststo: arch returns_mrk, arch(1) het(sanct)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  633.62574  
Iteration 1:   log likelihood =  634.75143  
Iteration 2:   log likelihood =  635.56041  
Iteration 3:   log likelihood =   635.6495  
Iteration 4:   log likelihood =  635.66992  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  635.67127  
Iteration 6:   log likelihood =  635.67142  
Iteration 7:   log likelihood =  635.67145  

ARCH family regression -- multiplicative heteroskedasticity

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  635.6714                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 returns_mrk |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_mrk  |
       _cons |   .0000302   .0011942     0.03   0.980    -.0023103    .0023707
-------------+----------------------------------------------------------------
HET          |
       sanct |   .5894011   .2969469     1.98   0.047     .0073959    1.171406
       _cons |  -7.846885   .1271893   -61.69   0.000    -8.096172   -7.597599
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |  -.0893579   .0712359    -1.25   0.210    -.2289778    .0502619
------------------------------------------------------------------------------
(est7 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    35.7767
 Prob > chi2(40)           =     0.6608

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    46.2854
 Prob > chi2(40)           =     0.2288

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sanc Sanctions) nomtitles titl
> e(Merck & Co. 1999 (Spain)) nodep  

Merck & Co. 1999 (Spain)
-----------------------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)          (7)   
-----------------------------------------------------------------------------------------------------
returns_~k                                                                                           
Constant      -0.000       -0.000       -0.000       -0.000       -0.000       -0.000        0.000   
             (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)   
-----------------------------------------------------------------------------------------------------
SIGMA2                                                                                               
Constant       0.000***     0.000***     0.000***     0.000***                                       
             (0.000)      (0.000)      (0.000)      (0.000)                                          
-----------------------------------------------------------------------------------------------------
ARMA                                                                                                 
L.ar                        0.069                    -0.071                                          
                          (0.068)                   (0.930)                                          
L.ma                                     0.070        0.140                                          
                                       (0.065)      (0.909)                                          
-----------------------------------------------------------------------------------------------------
ARCH                                                                                                 
L3.arch                                                            0.073                             
                                                                 (0.069)                             
L.arch                                                                                      -0.089   
                                                                                           (0.071)   
Constant                                                           0.000***                          
                                                                 (0.000)                             
-----------------------------------------------------------------------------------------------------
HET                                                                                                  
sanct                                                                           0.585*       0.589** 
                                                                              (0.309)      (0.297)   
Constant                                                                       -7.932***    -7.847***
                                                                              (0.104)      (0.127)   
-----------------------------------------------------------------------------------------------------
N                252          252          252          252          252          252          252   
aic        -1260.679    -1259.867    -1259.885    -1257.891    -1260.560    -1263.166    -1263.343   
bic        -1253.620    -1249.279    -1249.297    -1243.773    -1249.971    -1252.577    -1249.225   
-----------------------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/mrk1999.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sa
> nc Sanctions) nomtitles title(Merck & Co. 1999 (Spain)) nodep replace 
(note: file rawtables/mrk1999.tex not found)
(output written to rawtables/mrk1999.tex)

.           
.   * Perrigo 1999 (Spain)
.   
.     * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen returns_prgo = ln(prgo_close/prgo_close[_n-1])      
(6,026 missing values generated)

. 
.         * Sanctions Variable    
.           gen sanct = 1 if date > td("05apr1999") & date < td("11may1999")
(13,069 missing values generated)

.           recode sanct(.=0)
(sanct: 13069 changes made)

.           
.         * Limit Time Period
.           keep if date > td("31dec1998") & date < td("01jan2000")         
(12,842 observations deleted)

.           
.         * Set for analysis
.           tsset t
        time variable:  t, 7806 to 8057
                delta:  1 unit

.   
.         * Table A.40 Perrigo 1999 (Spain)
.           eststo clear 

.           
.           eststo: arch returns_prgo

(setting optimization to BHHH)
Iteration 0:   log likelihood =  541.60165  
Iteration 1:   log likelihood =  541.60165  

Time-series regression

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  541.6017                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
returns_prgo |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |  -.0003838   .0017779    -0.22   0.829    -.0038685    .0031008
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0007957   .0000525    15.16   0.000     .0006929    .0008985
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    52.0328
 Prob > chi2(40)           =     0.0963

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =   109.8354
 Prob > chi2(40)           =     0.0000

.           
.           eststo: arch returns_prgo, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =   543.8935  
Iteration 1:   log likelihood =  545.59095  
Iteration 2:   log likelihood =  545.64738  
Iteration 3:   log likelihood =  545.64861  
Iteration 4:   log likelihood =  545.64866  

Time-series regression -- AR disturbances

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =      16.50
Log likelihood =  545.6487                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
returns_prgo |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_prgo |
       _cons |  -.0003973   .0014852    -0.27   0.789    -.0033081    .0025136
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |  -.1779178   .0438018    -4.06   0.000    -.2637678   -.0920678
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0007706   .0000522    14.75   0.000     .0006683     .000873
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    46.1279
 Prob > chi2(40)           =     0.2337

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =   118.4962
 Prob > chi2(40)           =     0.0000

.           
.           eststo: arch returns_prgo, ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  545.04246  
Iteration 1:   log likelihood =  547.06921  
Iteration 2:   log likelihood =  547.15588  
Iteration 3:   log likelihood =  547.16453  
Iteration 4:   log likelihood =  547.16701  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  547.16756  
Iteration 6:   log likelihood =  547.16775  
Iteration 7:   log likelihood =  547.16776  

Time-series regression -- MA disturbances

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =      34.21
Log likelihood =  547.1678                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
returns_prgo |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_prgo |
       _cons |   -.000402   .0012947    -0.31   0.756    -.0029395    .0021355
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |  -.2570466   .0439464    -5.85   0.000      -.34318   -.1709133
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0007613   .0000509    14.95   0.000     .0006615    .0008611
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    44.2049
 Prob > chi2(40)           =     0.2985

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =   111.8959
 Prob > chi2(40)           =     0.0000

.           
.           eststo: arch returns_prgo, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  546.04562  
Iteration 1:   log likelihood =  548.81544  
Iteration 2:   log likelihood =  550.47229  
Iteration 3:   log likelihood =  550.64449  
Iteration 4:   log likelihood =  550.66095  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  550.66345  
Iteration 6:   log likelihood =  550.66409  
Iteration 7:   log likelihood =   550.6641  

Time-series regression -- ARMA disturbances

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =      85.11
Log likelihood =  550.6641                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
returns_prgo |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_prgo |
       _cons |  -.0004061   .0009668    -0.42   0.674     -.002301    .0014888
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |    .445943   .1519035     2.94   0.003     .1482176    .7436684
             |
          ma |
         L1. |  -.6907273   .1320171    -5.23   0.000    -.9494759   -.4319786
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0007405    .000049    15.12   0.000     .0006445    .0008365
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    36.2810
 Prob > chi2(40)           =     0.6384

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    99.4004
 Prob > chi2(40)           =     0.0000

.           
.           eststo: arch returns_prgo, ar(1) ma(1) arch(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  555.71418  
Iteration 1:   log likelihood =  558.07457  
Iteration 2:   log likelihood =  559.67335  
Iteration 3:   log likelihood =  559.95084  
Iteration 4:   log likelihood =  559.97146  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  559.97627  
Iteration 6:   log likelihood =  559.98223  
Iteration 7:   log likelihood =  559.98276  
Iteration 8:   log likelihood =  559.98278  

ARCH family regression -- ARMA disturbances

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =      49.11
Log likelihood =  559.9828                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
returns_prgo |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_prgo |
       _cons |  -.0010731   .0009926    -1.08   0.280    -.0030184    .0008723
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .4549369   .1966879     2.31   0.021     .0694356    .8404382
             |
          ma |
         L1. |  -.6701598   .1479072    -4.53   0.000    -.9600525   -.3802671
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .2408096   .0831297     2.90   0.004     .0778783    .4037409
             |
       _cons |   .0005543   .0000463    11.97   0.000     .0004636    .0006451
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    34.6292
 Prob > chi2(40)           =     0.7103

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    55.2609
 Prob > chi2(40)           =     0.0548

.           
.           eststo: arch returns_prgo, ar(1) ma(1) arch(1,4)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  558.54276  
Iteration 1:   log likelihood =  561.02593  
Iteration 2:   log likelihood =  562.04474  
Iteration 3:   log likelihood =  562.97495  
Iteration 4:   log likelihood =  563.04349  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  563.32529  
Iteration 6:   log likelihood =  563.46621  
Iteration 7:   log likelihood =  563.47172  
Iteration 8:   log likelihood =  563.47214  
Iteration 9:   log likelihood =  563.47216  

ARCH family regression -- ARMA disturbances

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =      28.19
Log likelihood =  563.4722                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
returns_prgo |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_prgo |
       _cons |  -.0012112   .0010016    -1.21   0.227    -.0031744     .000752
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .4964743    .218806     2.27   0.023     .0676225    .9253262
             |
          ma |
         L1. |  -.6708831   .1908817    -3.51   0.000    -1.045004   -.2967618
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .2720202   .0919279     2.96   0.003     .0918448    .4521956
         L4. |   .1631406   .0675955     2.41   0.016     .0306559    .2956253
             |
       _cons |   .0004281   .0000543     7.89   0.000     .0003217    .0005344
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    31.4159
 Prob > chi2(40)           =     0.8321

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    32.2838
 Prob > chi2(40)           =     0.8022

.           
.           eststo: arch returns_prgo, ar(1) ma(1) arch(1,4) het(sanct)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  558.60208  
Iteration 1:   log likelihood =  560.83764  
Iteration 2:   log likelihood =  562.52455  
Iteration 3:   log likelihood =  563.19985  
Iteration 4:   log likelihood =   563.7525  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  563.93348  
Iteration 6:   log likelihood =  564.41958  
Iteration 7:   log likelihood =  564.47501  
Iteration 8:   log likelihood =  564.49796  
Iteration 9:   log likelihood =  564.49848  
Iteration 10:  log likelihood =  564.49851  
Iteration 11:  log likelihood =  564.49851  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =      26.44
Log likelihood =  564.4985                        Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |                 OPG
returns_prgo |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_prgo |
       _cons |   -.001263   .0009893    -1.28   0.202    -.0032021    .0006761
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .4759744   .2366035     2.01   0.044       .01224    .9397088
             |
          ma |
         L1. |  -.6579282   .2079871    -3.16   0.002    -1.065575    -.250281
-------------+----------------------------------------------------------------
HET          |
       sanct |   .5955392   .4646455     1.28   0.200    -.3151492    1.506228
       _cons |  -7.792471   .1268586   -61.43   0.000     -8.04111   -7.543833
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .2609903   .0983195     2.65   0.008     .0682876    .4536931
         L4. |   .1472369   .0640533     2.30   0.022     .0216948     .272779
------------------------------------------------------------------------------
(est7 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    30.5906
 Prob > chi2(40)           =     0.8581

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    32.8280
 Prob > chi2(40)           =     0.7822

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sanc Sanctions) nomtitles titl
> e(Perrigo 1999 (Spain)) nodep  

Perrigo 1999 (Spain)
-----------------------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)          (7)   
-----------------------------------------------------------------------------------------------------
returns_~o                                                                                           
Constant      -0.000       -0.000       -0.000       -0.000       -0.001       -0.001       -0.001   
             (0.002)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)   
-----------------------------------------------------------------------------------------------------
SIGMA2                                                                                               
Constant       0.001***     0.001***     0.001***     0.001***                                       
             (0.000)      (0.000)      (0.000)      (0.000)                                          
-----------------------------------------------------------------------------------------------------
ARMA                                                                                                 
L.ar                       -0.178***                  0.446***     0.455**      0.496**      0.476** 
                          (0.044)                   (0.152)      (0.197)      (0.219)      (0.237)   
L.ma                                    -0.257***    -0.691***    -0.670***    -0.671***    -0.658***
                                       (0.044)      (0.132)      (0.148)      (0.191)      (0.208)   
-----------------------------------------------------------------------------------------------------
ARCH                                                                                                 
L.arch                                                             0.241***     0.272***     0.261***
                                                                 (0.083)      (0.092)      (0.098)   
L4.arch                                                                         0.163**      0.147** 
                                                                              (0.068)      (0.064)   
Constant                                                           0.001***     0.000***             
                                                                 (0.000)      (0.000)                
-----------------------------------------------------------------------------------------------------
HET                                                                                                  
sanct                                                                                        0.596   
                                                                                           (0.465)   
Constant                                                                                    -7.792***
                                                                                           (0.127)   
-----------------------------------------------------------------------------------------------------
N                252          252          252          252          252          252          252   
aic        -1079.203    -1085.297    -1088.336    -1093.328    -1109.966    -1114.944    -1114.997   
bic        -1072.144    -1074.709    -1077.747    -1079.210    -1092.318    -1093.768    -1090.291   
-----------------------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/prgo1999.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant s
> anc Sanctions) nomtitles title(Perrigo 1999 (Spain)) nodep replace 
(note: file rawtables/prgo1999.tex not found)
(output written to rawtables/prgo1999.tex)

. 
.   * Pfizer 1999 (Spain)
.   
.     * Clear
.           clear

.           
.     * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen returns_pfe = ln(pfe_close/pfe_close[_n-1])         
(3,148 missing values generated)

. 
.         * Sanctions Variable    
.           gen sanct = 1 if date > td("05apr1999") & date < td("11may1999")
(13,069 missing values generated)

.           recode sanct(.=0)
(sanct: 13069 changes made)

.           
.         * Limit Time Period
.           keep if date > td("31dec1998") & date < td("01jan2000")         
(12,842 observations deleted)

.           
.         * Set for analysis
.           tsset t
        time variable:  t, 7806 to 8057
                delta:  1 unit

.   
.         * Table A.41 Pfizer 1999 (Spain)
.           eststo clear

.           
.           eststo: arch returns_pfe

(setting optimization to BHHH)
Iteration 0:   log likelihood =  577.31995  
Iteration 1:   log likelihood =  577.31995  

Time-series regression

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  577.3199                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 returns_pfe |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |  -.0009936   .0015634    -0.64   0.525    -.0040577    .0020705
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0005993   .0000417    14.37   0.000     .0005176     .000681
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    61.2314
 Prob > chi2(40)           =     0.0170

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    33.7143
 Prob > chi2(40)           =     0.7478

.           
.           eststo: arch returns_pfe, ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  580.54696  
Iteration 1:   log likelihood =  581.11938  
Iteration 2:   log likelihood =  581.18099  
Iteration 3:   log likelihood =   581.1867  
Iteration 4:   log likelihood =  581.18723  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  581.18729  
Iteration 6:   log likelihood =  581.18729  

Time-series regression -- AR disturbances

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       8.82
Log likelihood =  581.1873                        Prob > chi2     =     0.0030

------------------------------------------------------------------------------
             |                 OPG
 returns_pfe |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_pfe  |
       _cons |  -.0010102   .0018665    -0.54   0.588    -.0046686    .0026481
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .1738837    .058544     2.97   0.003     .0591395    .2886279
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0005812   .0000442    13.16   0.000     .0004946    .0006677
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.6551
 Prob > chi2(40)           =     0.4414

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    30.5148
 Prob > chi2(40)           =     0.8604

.           
.           eststo: arch returns_pfe, ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =   580.1114  
Iteration 1:   log likelihood =  580.78443  
Iteration 2:   log likelihood =  580.85385  
Iteration 3:   log likelihood =   580.8626  
Iteration 4:   log likelihood =  580.86358  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  580.86366  
Iteration 6:   log likelihood =  580.86367  

Time-series regression -- MA disturbances

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(1)    =       7.24
Log likelihood =  580.8637                        Prob > chi2     =     0.0071

------------------------------------------------------------------------------
             |                 OPG
 returns_pfe |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_pfe  |
       _cons |  -.0010059   .0017811    -0.56   0.572    -.0044967    .0024849
-------------+----------------------------------------------------------------
ARMA         |
          ma |
         L1. |   .1579266   .0586911     2.69   0.007     .0428941    .2729591
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0005827   .0000437    13.35   0.000     .0004971    .0006682
------------------------------------------------------------------------------
(est3 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    42.4981
 Prob > chi2(40)           =     0.3639

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    31.1422
 Prob > chi2(40)           =     0.8410

.           
.           eststo: arch returns_pfe, ar(1) ma(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  580.44195  
Iteration 1:   log likelihood =  581.09204  
Iteration 2:   log likelihood =  581.15732  
Iteration 3:   log likelihood =  581.17376  
Iteration 4:   log likelihood =  581.18021  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  581.18326  
Iteration 6:   log likelihood =  581.20066  
Iteration 7:   log likelihood =  581.20087  
Iteration 8:   log likelihood =  581.20089  

Time-series regression -- ARMA disturbances

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =       9.02
Log likelihood =  581.2009                        Prob > chi2     =     0.0110

------------------------------------------------------------------------------
             |                 OPG
 returns_pfe |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_pfe  |
       _cons |   -.001012   .0019162    -0.53   0.597    -.0047677    .0027437
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .2114393   .3099849     0.68   0.495      -.39612    .8189987
             |
          ma |
         L1. |  -.0385626   .3158279    -0.12   0.903    -.6575739    .5804487
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0005811   .0000442    13.15   0.000     .0004945    .0006677
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.6177
 Prob > chi2(40)           =     0.4431

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    30.4163
 Prob > chi2(40)           =     0.8633

.           
.           eststo: arch returns_pfe, ar(1,5)

(setting optimization to BHHH)
Iteration 0:   log likelihood =   583.7679  
Iteration 1:   log likelihood =  584.10816  
Iteration 2:   log likelihood =  584.13526  
Iteration 3:   log likelihood =  584.13829  
Iteration 4:   log likelihood =  584.13864  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  584.13869  
Iteration 6:   log likelihood =   584.1387  

Time-series regression -- AR disturbances

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =      16.35
Log likelihood =  584.1387                        Prob > chi2     =     0.0003

------------------------------------------------------------------------------
             |                 OPG
 returns_pfe |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_pfe  |
       _cons |  -.0009663    .001543    -0.63   0.531    -.0039906    .0020579
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .1547638   .0600963     2.58   0.010     .0369771    .2725505
         L5. |   -.151701   .0622103    -2.44   0.015    -.2736309    -.029771
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0005677   .0000441    12.86   0.000     .0004812    .0006542
------------------------------------------------------------------------------
(est5 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    31.3907
 Prob > chi2(40)           =     0.8329

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    31.1479
 Prob > chi2(40)           =     0.8408

.           
.           eststo: arch returns_pfe, ar(1,5) het(sanct)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  581.56726  
Iteration 1:   log likelihood =  584.63066  
Iteration 2:   log likelihood =  585.69869  
Iteration 3:   log likelihood =  586.29131  
Iteration 4:   log likelihood =  586.50205  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  586.55755  
Iteration 6:   log likelihood =  586.57443  
Iteration 7:   log likelihood =  586.57445  

Time-series regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 7806 - 8057                               Number of obs   =        252
Distribution: Gaussian                            Wald chi2(2)    =      14.15
Log likelihood =  586.5745                        Prob > chi2     =     0.0008

------------------------------------------------------------------------------
             |                 OPG
 returns_pfe |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
returns_pfe  |
       _cons |  -.0006016   .0015465    -0.39   0.697    -.0036326    .0024295
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .1460679   .0578576     2.52   0.012     .0326692    .2594666
         L5. |  -.1342126   .0620185    -2.16   0.030    -.2557667   -.0126586
-------------+----------------------------------------------------------------
HET          |
       sanct |   .6163088   .2209395     2.79   0.005     .1832753    1.049342
       _cons |  -7.554391   .0915591   -82.51   0.000    -7.733843   -7.374938
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2       

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    30.8218
 Prob > chi2(40)           =     0.8511

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    34.3148
 Prob > chi2(40)           =     0.7234

.           
.           esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sanc Sanctions) nomtitles titl
> e(Pfizer 1999 (Spain)) nodep  

Pfizer 1999 (Spain)
----------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)   
----------------------------------------------------------------------------------------
returns_~e                                                                              
Constant      -0.001       -0.001       -0.001       -0.001       -0.001       -0.001   
             (0.002)      (0.002)      (0.002)      (0.002)      (0.002)      (0.002)   
----------------------------------------------------------------------------------------
SIGMA2                                                                                  
Constant       0.001***     0.001***     0.001***     0.001***     0.001***             
             (0.000)      (0.000)      (0.000)      (0.000)      (0.000)                
----------------------------------------------------------------------------------------
ARMA                                                                                    
L.ar                        0.174***                  0.211        0.155**      0.146** 
                          (0.059)                   (0.310)      (0.060)      (0.058)   
L5.ar                                                             -0.152**     -0.134** 
                                                                 (0.062)      (0.062)   
L.ma                                     0.158***    -0.039                             
                                       (0.059)      (0.316)                             
----------------------------------------------------------------------------------------
HET                                                                                     
sanct                                                                           0.616***
                                                                              (0.221)   
Constant                                                                       -7.554***
                                                                              (0.092)   
----------------------------------------------------------------------------------------
N                252          252          252          252          252          252   
aic        -1150.640    -1156.375    -1155.727    -1154.402    -1160.277    -1163.149   
bic        -1143.581    -1145.786    -1145.139    -1140.284    -1146.160    -1145.502   
----------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.           esttab using rawtables/pfe1999.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant sa
> nc Sanctions) nomtitles title(Pfizer 1999 (Spain)) nodep replace 
(note: file rawtables/pfe1999.tex not found)
(output written to rawtables/pfe1999.tex)

.           
. * Close log file
.   log close       
      name:  <unnamed>
       log:  /Users/claywebb/Dropbox/KU/Research/Sanctions/The Domestic Economic Costs of Sanctions - A Firm Level Analysis/Replication Materials f
> or The Domestic Economic Costs of Sanctions - A Firm Level Analysis/thedomesticcostsofsanctions.log
  log type:  text
 closed on:  11 Jun 2020, 23:48:37
---------------------------------------------------------------------------------------------------------------------------------------------------
